Project Description: Analysis of 16S rRNA amplicon data from a cohort of human volunteers (heatlhy men aged 18-35) randomized into three treatment groups: 1) control (no antibiotics), 2) narrow spectrum (oral vancomycin) or 3) broad-spectrum (oral vancomycin, ciprofloxacin, and metronidazole) antibiotics. Antibiotics were taken for 7-days prior to vaccination with Rotarix (RVV), polysaccharide-pneumococcal (Pneumo 23) and tetanus-toxoid vaccine. The primary endpoint was difference in 28 days-post-vaccination anti-RV IgA. Secondary endpoints were proportion of volunteers with day 7 anti-RV IgA boosting (>=2 fold-increase), absolute and proportion of RV-antigen shedding, anti-RV, pneumococcal and anti-tetanus IgG.

Primary Collaborator: Vanessa Harris (v.harris@aighd.org)

Other relevant files: The following Phyloseq objects are available. Each is distinguished based on the 16S reference database used for taxonomic classification. RDP and Silva were processed through the species assignment workflow:

Workflow details: The R commands below represent a full analysis of the following:

  1. Sample assessment
  2. ASV properties
  3. Community composition
  4. Alpha diversity
  5. Beta diversity
  6. Differential abundance testing
# Set default knitr option
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# ggplot2 settings
# Set global theming
theme_set(theme_bw(base_size = 12))

# Set a seed value so that exact results can be reproduced
set.seed(1000)

Read in data

# Load Phyloseq Object
# Selected RDP due to it's up-to-date nature and conservative taxonomy. Other files are also valid for anlysis but are not explored here but are available in the ./data/ directory
ps0 <- readRDS("~/Dropbox/Research/human_volunteer/data/ps0.human_volunteer.rdp.RDS")
ps0
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1936 taxa and 122 samples ]
## tax_table()   Taxonomy Table:    [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]
# Load mapping file
map <- import_qiime_sample_data("~/Dropbox/Research/human_volunteer/data/mapping_human_volunteer.txt")
ps0 <- merge_phyloseq(ps0, map)
ps0
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1936 taxa and 122 samples ]
## sample_data() Sample Data:       [ 122 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]
# View sample variables & generate basic stats
sample_variables(ps0)
##   [1] "X.SampleID"                  "BarcodeSequence"            
##   [3] "LinkerPrimerSequence"        "sequence_ID"                
##   [5] "miseq_run"                   "plate"                      
##   [7] "well"                        "collaborator"               
##   [9] "tissue"                      "number_seqs_slout"          
##  [11] "sample_surviving_dada2"      "sample_ID"                  
##  [13] "tube_ID"                     "patient_ID"                 
##  [15] "randomization"               "day"                        
##  [17] "randomization_arm"           "antibiotics"                
##  [19] "Date_assessment"             "Age"                        
##  [21] "Gender"                      "Ethnicity"                  
##  [23] "Asthma"                      "Eczema"                     
##  [25] "Hayfever"                    "Allergies"                  
##  [27] "Def_allergies"               "Other_illness"              
##  [29] "Chronic_medication"          "Def_medication"             
##  [31] "Herbs_OTC"                   "def_Herbs_OTC"              
##  [33] "Alcohol"                     "Alcoholunitsweek"           
##  [35] "Smoking"                     "Drugs"                      
##  [37] "Drugstype"                   "Drugsusage"                 
##  [39] "Weight"                      "Length"                     
##  [41] "BMIcalc"                     "SystolicBP"                 
##  [43] "DiastolicBP"                 "Pulse"                      
##  [45] "Respfreq"                    "Physical_exam_abnorm"       
##  [47] "def_phys_abnormalities"      "Lab_abnorm_screen"          
##  [49] "def_abnorm_lab_screen"       "Serum_screen"               
##  [51] "PBMC_screen"                 "Feces_screen"               
##  [53] "Date_day_9"                  "New_medication_day_9"       
##  [55] "def_new_med_day_9"           "Course_completed"           
##  [57] "Antibiotic_side_effects"     "Def_abx_side_effects"       
##  [59] "Lab_abnorm_post_antibiotics" "def_abnorm_post_antibiotics"
##  [61] "Date_day_0"                  "Feces_day_0"                
##  [63] "New_medication_day0"         "Def_new_medication_day_0"   
##  [65] "Vaccination_side_effects"    "Def_vac_side_effects"       
##  [67] "Lab_abnorm_post_vaccination" "def_abnorm_post_vaccination"
##  [69] "Date_day_7"                  "Feces_day_1"                
##  [71] "Feces_day_2"                 "Feces_day_3"                
##  [73] "Feces_day_4"                 "Feces_day_5"                
##  [75] "Feces_day_6"                 "Feces_day_7"                
##  [77] "Serum_day_7"                 "New_medication_day7"        
##  [79] "def_new_med_day7"            "Date_day_14"                
##  [81] "New_medication_day14"        "def_new_med_day14"          
##  [83] "Serum_day_14"                "PBMC_day_14"                
##  [85] "Date_day_28"                 "New_medication_day28"       
##  [87] "def_new_med_day28"           "Serum_day_28"               
##  [89] "PBMC_day_28"                 "Study_complete"             
##  [91] "pre_RV_IgA"                  "d7_RV_IgA"                  
##  [93] "boost_change"                "d7_RV_IgA_boost_fc"         
##  [95] "d7_roto_boost"               "d7_rota_boost_updated"      
##  [97] "d14_RV_IgA"                  "d28_RV_IgA"                 
##  [99] "RV_Ag_shedding_d0"           "RV_Ag_shedding_d1"          
## [101] "RV_Ag_shedding_d2"           "RV_Ag_shedding_d3"          
## [103] "RV_Ag_shedding_d4"           "RV_Ag_shedding_d5"          
## [105] "RV_Ag_shedding_d6"           "Shedding"                   
## [107] "pneumo_IgG_pre"              "pneumo_IgG_7"               
## [109] "pneumo_IgG_14"               "pneumo_IgG_28"              
## [111] "pneumo_IgG_7vPre"            "pneumo_IgG_14vPre"          
## [113] "pneumo_IgG_28vPre"           "tetanus_IgG_pre"            
## [115] "tetanus_IgG_d7"              "tetanus_IgG_d14"            
## [117] "tetanus_IgG_d28"             "tetanus_IgG_7vPre"          
## [119] "tetanus_IgG_14vPre"          "tetanus_IgG_28vPre"
sd(sample_sums(ps0))
## [1] 23974.15
get_taxa_unique(ps0, "Phylum")
##  [1] "Bacteroidetes"             "Firmicutes"               
##  [3] "Verrucomicrobia"           "Proteobacteria"           
##  [5] "Fusobacteria"              "Synergistetes"            
##  [7] "Actinobacteria"            "Spirochaetes"             
##  [9] NA                          "Lentisphaerae"            
## [11] "Euryarchaeota"             "Elusimicrobia"            
## [13] "Cyanobacteria/Chloroplast" "Tenericutes"
ntaxa(ps0)
## [1] 1936

Factor reordering and renaming

# Order and update randomization arms factor
sample_data(ps0)$randomization_arm
##   [1] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##   [3] No_antibiotics              Broad_spectrum_antibiotics 
##   [5] No_antibiotics              No_antibiotics             
##   [7] No_antibiotics              Narrow_spectrum_antibiotics
##   [9] No_antibiotics              Broad_spectrum_antibiotics 
##  [11] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [13] Narrow_spectrum_antibiotics No_antibiotics             
##  [15] Narrow_spectrum_antibiotics No_antibiotics             
##  [17] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [19] No_antibiotics              Broad_spectrum_antibiotics 
##  [21] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [23] Broad_spectrum_antibiotics  No_antibiotics             
##  [25] Narrow_spectrum_antibiotics No_antibiotics             
##  [27] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [29] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [31] Narrow_spectrum_antibiotics No_antibiotics             
##  [33] No_antibiotics              Broad_spectrum_antibiotics 
##  [35] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [37] Broad_spectrum_antibiotics  Broad_spectrum_antibiotics 
##  [39] No_antibiotics              No_antibiotics             
##  [41] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [43] Broad_spectrum_antibiotics  No_antibiotics             
##  [45] No_antibiotics              No_antibiotics             
##  [47] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [49] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [51] No_antibiotics              Broad_spectrum_antibiotics 
##  [53] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [55] No_antibiotics              No_antibiotics             
##  [57] Narrow_spectrum_antibiotics No_antibiotics             
##  [59] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [61] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [63] No_antibiotics              No_antibiotics             
##  [65] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [67] No_antibiotics              Broad_spectrum_antibiotics 
##  [69] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [71] Broad_spectrum_antibiotics  No_antibiotics             
##  [73] Narrow_spectrum_antibiotics No_antibiotics             
##  [75] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [77] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [79] No_antibiotics              Narrow_spectrum_antibiotics
##  [81] Broad_spectrum_antibiotics  No_antibiotics             
##  [83] No_antibiotics              Narrow_spectrum_antibiotics
##  [85] Broad_spectrum_antibiotics  Broad_spectrum_antibiotics 
##  [87] No_antibiotics              No_antibiotics             
##  [89] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [91] Broad_spectrum_antibiotics  No_antibiotics             
##  [93] No_antibiotics              No_antibiotics             
##  [95] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [97] Narrow_spectrum_antibiotics No_antibiotics             
##  [99] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
## [101] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [103] No_antibiotics              Narrow_spectrum_antibiotics
## [105] No_antibiotics              No_antibiotics             
## [107] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
## [109] No_antibiotics              Broad_spectrum_antibiotics 
## [111] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
## [113] No_antibiotics              Narrow_spectrum_antibiotics
## [115] No_antibiotics              Narrow_spectrum_antibiotics
## [117] Broad_spectrum_antibiotics  Broad_spectrum_antibiotics 
## [119] Narrow_spectrum_antibiotics No_antibiotics             
## [121] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
## 3 Levels: Broad_spectrum_antibiotics ... No_antibiotics
sample_data(ps0)$randomization_arm <- factor(sample_data(ps0)$randomization_arm, levels = c("No_antibiotics", "Narrow_spectrum_antibiotics", "Broad_spectrum_antibiotics"))
sample_data(ps0)$randomization_arm
##   [1] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##   [3] No_antibiotics              Broad_spectrum_antibiotics 
##   [5] No_antibiotics              No_antibiotics             
##   [7] No_antibiotics              Narrow_spectrum_antibiotics
##   [9] No_antibiotics              Broad_spectrum_antibiotics 
##  [11] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [13] Narrow_spectrum_antibiotics No_antibiotics             
##  [15] Narrow_spectrum_antibiotics No_antibiotics             
##  [17] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [19] No_antibiotics              Broad_spectrum_antibiotics 
##  [21] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [23] Broad_spectrum_antibiotics  No_antibiotics             
##  [25] Narrow_spectrum_antibiotics No_antibiotics             
##  [27] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [29] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [31] Narrow_spectrum_antibiotics No_antibiotics             
##  [33] No_antibiotics              Broad_spectrum_antibiotics 
##  [35] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [37] Broad_spectrum_antibiotics  Broad_spectrum_antibiotics 
##  [39] No_antibiotics              No_antibiotics             
##  [41] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [43] Broad_spectrum_antibiotics  No_antibiotics             
##  [45] No_antibiotics              No_antibiotics             
##  [47] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [49] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [51] No_antibiotics              Broad_spectrum_antibiotics 
##  [53] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [55] No_antibiotics              No_antibiotics             
##  [57] Narrow_spectrum_antibiotics No_antibiotics             
##  [59] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [61] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [63] No_antibiotics              No_antibiotics             
##  [65] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [67] No_antibiotics              Broad_spectrum_antibiotics 
##  [69] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [71] Broad_spectrum_antibiotics  No_antibiotics             
##  [73] Narrow_spectrum_antibiotics No_antibiotics             
##  [75] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [77] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
##  [79] No_antibiotics              Narrow_spectrum_antibiotics
##  [81] Broad_spectrum_antibiotics  No_antibiotics             
##  [83] No_antibiotics              Narrow_spectrum_antibiotics
##  [85] Broad_spectrum_antibiotics  Broad_spectrum_antibiotics 
##  [87] No_antibiotics              No_antibiotics             
##  [89] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
##  [91] Broad_spectrum_antibiotics  No_antibiotics             
##  [93] No_antibiotics              No_antibiotics             
##  [95] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
##  [97] Narrow_spectrum_antibiotics No_antibiotics             
##  [99] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
## [101] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [103] No_antibiotics              Narrow_spectrum_antibiotics
## [105] No_antibiotics              No_antibiotics             
## [107] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
## [109] No_antibiotics              Broad_spectrum_antibiotics 
## [111] Broad_spectrum_antibiotics  Narrow_spectrum_antibiotics
## [113] No_antibiotics              Narrow_spectrum_antibiotics
## [115] No_antibiotics              Narrow_spectrum_antibiotics
## [117] Broad_spectrum_antibiotics  Broad_spectrum_antibiotics 
## [119] Narrow_spectrum_antibiotics No_antibiotics             
## [121] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics 
## 3 Levels: No_antibiotics ... Broad_spectrum_antibiotics
sample_data(ps0)$randomization_arm <- factor(sample_data(ps0)$randomization_arm, labels = c("No antibiotics", "Narrow spectrum antibiotics", "Broad spectrum antibiotics"))
sample_data(ps0)$randomization_arm
##   [1] Narrow spectrum antibiotics Broad spectrum antibiotics 
##   [3] No antibiotics              Broad spectrum antibiotics 
##   [5] No antibiotics              No antibiotics             
##   [7] No antibiotics              Narrow spectrum antibiotics
##   [9] No antibiotics              Broad spectrum antibiotics 
##  [11] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [13] Narrow spectrum antibiotics No antibiotics             
##  [15] Narrow spectrum antibiotics No antibiotics             
##  [17] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [19] No antibiotics              Broad spectrum antibiotics 
##  [21] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [23] Broad spectrum antibiotics  No antibiotics             
##  [25] Narrow spectrum antibiotics No antibiotics             
##  [27] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [29] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [31] Narrow spectrum antibiotics No antibiotics             
##  [33] No antibiotics              Broad spectrum antibiotics 
##  [35] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [37] Broad spectrum antibiotics  Broad spectrum antibiotics 
##  [39] No antibiotics              No antibiotics             
##  [41] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [43] Broad spectrum antibiotics  No antibiotics             
##  [45] No antibiotics              No antibiotics             
##  [47] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [49] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [51] No antibiotics              Broad spectrum antibiotics 
##  [53] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [55] No antibiotics              No antibiotics             
##  [57] Narrow spectrum antibiotics No antibiotics             
##  [59] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [61] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [63] No antibiotics              No antibiotics             
##  [65] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [67] No antibiotics              Broad spectrum antibiotics 
##  [69] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [71] Broad spectrum antibiotics  No antibiotics             
##  [73] Narrow spectrum antibiotics No antibiotics             
##  [75] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [77] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [79] No antibiotics              Narrow spectrum antibiotics
##  [81] Broad spectrum antibiotics  No antibiotics             
##  [83] No antibiotics              Narrow spectrum antibiotics
##  [85] Broad spectrum antibiotics  Broad spectrum antibiotics 
##  [87] No antibiotics              No antibiotics             
##  [89] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [91] Broad spectrum antibiotics  No antibiotics             
##  [93] No antibiotics              No antibiotics             
##  [95] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [97] Narrow spectrum antibiotics No antibiotics             
##  [99] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [101] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [103] No antibiotics              Narrow spectrum antibiotics
## [105] No antibiotics              No antibiotics             
## [107] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [109] No antibiotics              Broad spectrum antibiotics 
## [111] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [113] No antibiotics              Narrow spectrum antibiotics
## [115] No antibiotics              Narrow spectrum antibiotics
## [117] Broad spectrum antibiotics  Broad spectrum antibiotics 
## [119] Narrow spectrum antibiotics No antibiotics             
## [121] Narrow spectrum antibiotics Broad spectrum antibiotics 
## 3 Levels: No antibiotics ... Broad spectrum antibiotics

Sample assessment

# Format a data table to combine sample summary data with sample variable data
ss <- sample_sums(ps0)
sd <- as.data.frame(sample_data(ps0)) # useful to coerce the phyloseq object into an R data frame
ss.df <- merge(sd, data.frame("ASV" = ss), by ="row.names") # merge ss with sd by row names. Rename ss to ASVs in the new data frame

# Plot the data by the treatment variable
y = 100 # Set a threshold for the minimum number of acceptable reads. Can start as a guess
x = "randomization_arm" # Set the x-axis variable you want to examine
label = "Description" # This is the label you want to overlay on the points that are below threshold y. Should be something sample specific

# Plot
p.ss.boxplot<- ggplot(ss.df, aes_string(x, y = "ASV")) + # x is what you assigned it above
  geom_boxplot(outlier.colour="NA", aes(group = randomization_arm)) +
  scale_y_log10() +
  geom_hline(yintercept = y, lty = 2) + # Draws a dashed line across the threshold you set above as y
  geom_jitter(alpha = 0.6, width = 0.15, size = 3) +
  labs(y = "ASV (log10)") +
  facet_grid(~day) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme(legend.position = "NULL") +
  theme(axis.title.x = element_blank())
p.ss.boxplot

##Outlier sample removal

# Remove samples with fewer than y ASV
# Save samples that fell below the selected threshold
ps.failed <- prune_samples(sample_sums(ps0) < y, ps0)
nsamples(ps.failed)
## [1] 22
# Create a table of failed sample information
failed.samples <- data.frame(sample_names(ps.failed), sample_data(ps.failed)$sample_ID, sample_data(ps.failed)$tube_ID)
write.table(failed.samples, file = "./results/failed_samples.txt", sep = "\t")

# Remove samples with fewer than y ASV
ps0
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1936 taxa and 122 samples ]
## sample_data() Sample Data:       [ 122 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]
ps0 <- prune_samples(sample_sums(ps0) >=y, ps0)
ps0
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1936 taxa and 100 samples ]
## sample_data() Sample Data:       [ 100 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]
min(sample_sums(ps0))
## [1] 103

Taxon cleaning

# Begin by removing sequences that were not classified as Bacteria or were classified as either mitochondria or chlorplast
ps0 # Check the number of taxa prior to removal
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1936 taxa and 100 samples ]
## sample_data() Sample Data:       [ 100 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]
ps1 <- ps0 %>%
  subset_taxa(
    Kingdom == "Bacteria" &
    Family  != "mitochondria" &
    Class   != "Chloroplast" &
    Phylum != "Cyanobacteria/Chloroplast"
  )
ps1 # Confirm that the taxa were removed
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 100 samples ]
## sample_data() Sample Data:       [ 100 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]

Data transformations

# Some of these are not used in subsequent analysis, but are coded as a chunk here for convenience
# Transform to Realative abundances
ps1.ra <- transform_sample_counts(ps1, function(OTU) OTU/sum(OTU))

Subsetting

# No antibiotics (control)
ps1.con <- subset_samples(ps1, randomization_arm == "No antibiotics")
ps1.con
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 34 samples ]
## sample_data() Sample Data:       [ 34 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.con <- prune_samples(sample_sums(ps1.con) > 0, ps1.con)
ps1.con
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 34 samples ]
## sample_data() Sample Data:       [ 34 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.con.log <- transform_sample_counts(ps1.con, function(x) log(1 + x))

# Narrow spectrum antibiotics
ps1.narrow <- subset_samples(ps1, randomization_arm == "Narrow spectrum antibiotics")
ps1.narrow
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 36 samples ]
## sample_data() Sample Data:       [ 36 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.narrow <- prune_samples(sample_sums(ps1.narrow) > 0, ps1.narrow)
ps1.narrow
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 36 samples ]
## sample_data() Sample Data:       [ 36 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.narrow.log <- transform_sample_counts(ps1.narrow, function(x) log(1 + x))

# Broad spectrum antibiotics
ps1.broad <- subset_samples(ps1, randomization_arm == "Broad spectrum antibiotics")
ps1.broad
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 30 samples ]
## sample_data() Sample Data:       [ 30 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.broad <- prune_samples(sample_sums(ps1.broad) > 0, ps1.broad)
ps1.broad
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 30 samples ]
## sample_data() Sample Data:       [ 30 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.broad.log <- transform_sample_counts(ps1.broad, function(x) log(1 + x))

# Antibiotics
ps1.abx <- subset_samples(ps1, antibiotics == "Antibiotics")
ps1.abx
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 66 samples ]
## sample_data() Sample Data:       [ 66 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.abx <- prune_samples(sample_sums(ps1.abx) > 0, ps1.abx)
ps1.abx
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 66 samples ]
## sample_data() Sample Data:       [ 66 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.abx.log <- transform_sample_counts(ps1.abx, function(x) log(1 + x))

Community composition plotting

These plots were not included in the final manuscript but remain here as compositional overviews. Interactive plots are also produced.

# Phyla level plots
# Melt to long format (for ggploting) 
# Prune out phyla below % in each sample

# Set propotional threshold for taxa to be removed
threshold = 0.03

ps1_phylum <- ps1 %>%
  tax_glom(taxrank = "Phylum") %>%                     # agglomerate at phylum level
  transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
  psmelt() %>%                                         # Melt to long format
  filter(Abundance > threshold) %>%                    # Filter out low abundance taxa
  arrange(Phylum)                                      # Sort data frame alphabetically by phylum

# Convert Sample No to a factor because R is weird sometime
ps1_phylum$patient_ID <- as.factor(ps1_phylum$patient_ID)

p.comm.bar <- ggplot(ps1_phylum, aes(x = patient_ID, y = Abundance, fill = Phylum)) + 
  geom_bar(stat = "identity", width = 0.9) +
  facet_wrap(randomization_arm~day, scales = "free_x") +
  ggtitle("Community Composition Over Time and Randomization Arm") +
  labs(y = "Relative Abundance") +
  theme(legend.position = "bottom") +
  theme(axis.text.x = element_blank()) +
  theme(axis.title.x = element_blank()) +
  scale_fill_brewer(palette = "Dark2")
p.comm.bar

# Draw interactive plot
ggplotly(p.comm.bar)

##Phyla-level summary plots

# Agglomerate taxa
glom <- tax_glom(ps1.ra, taxrank = 'Phylum')

# Create dataframe from phyloseq object
dat <- as.tibble(psmelt(glom))

# Set comparisons
my_comparisons.1 <- list( c("No antibiotics", "Narrow spectrum antibiotics"), c("No antibiotics", "Broad spectrum antibiotics"), c("Narrow spectrum antibiotics", "Broad spectrum antibiotics") )
my_comparisons.2 <- list( c("-9", "0"), c("-9", "7"), c("0", "7") )

# Table of mean abundances per Phylum
dat.summary <- dat %>%
  group_by(Phylum) %>%
  summarise(mean = mean(Abundance)) %>%
  arrange(desc(mean))
ggplot(dat.summary, aes(x = fct_reorder(Phylum, mean), y = mean)) +
  geom_point() +
  coord_flip()

# Reorder
levels(dat$Phylum)
##  [1] "Actinobacteria"  "Bacteroidetes"   "Elusimicrobia"  
##  [4] "Firmicutes"      "Fusobacteria"    "Lentisphaerae"  
##  [7] "Proteobacteria"  "Spirochaetes"    "Synergistetes"  
## [10] "Tenericutes"     "Verrucomicrobia"
dat.reorder <- dat %>%
  mutate(Phylum = reorder(Phylum, Abundance, mean))
levels(dat.reorder$Phylum)
##  [1] "Tenericutes"     "Elusimicrobia"   "Lentisphaerae"  
##  [4] "Spirochaetes"    "Synergistetes"   "Fusobacteria"   
##  [7] "Actinobacteria"  "Verrucomicrobia" "Proteobacteria" 
## [10] "Bacteroidetes"   "Firmicutes"
# Determine how mean abundances of Phyla are altered by treatment
p.boxplot.phylum.1 <- ggboxplot(dat.reorder, x = "randomization_arm", y = "Abundance", outlier.shape = NA) +
  geom_jitter(width = 0.2) +
  labs(title = "Figure S3", y = "Relative Abundance", x = "") +
  coord_cartesian(ylim = c(0, 1.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(comparisons = my_comparisons.1, label = "p.signif", hide.ns = TRUE) +
  facet_grid(day~Phylum)
p.boxplot.phylum.1

# Note: For the figure in the manuscript we manually replaced the asterisks and removed the ns for aesthetic purposes

# Subset to phyla with treatment alteration in mean abundnaces
dat.1 <- filter(dat.reorder, Phylum %in% c("Firmicutes",
                                   "Bacteroidetes",
                                   "Proteobacteria",
                                   "Verrucomicrobia",
                                   "Actinobacteria",
                                   "Fusobacteria"))
levels(dat.1$Phylum)
##  [1] "Tenericutes"     "Elusimicrobia"   "Lentisphaerae"  
##  [4] "Spirochaetes"    "Synergistetes"   "Fusobacteria"   
##  [7] "Actinobacteria"  "Verrucomicrobia" "Proteobacteria" 
## [10] "Bacteroidetes"   "Firmicutes"
dat.1 <- droplevels(dat.1)
levels(dat.1$Phylum)
## [1] "Fusobacteria"    "Actinobacteria"  "Verrucomicrobia" "Proteobacteria" 
## [5] "Bacteroidetes"   "Firmicutes"
# Boxplot with treatment altered phyla
p.boxplot.phylum.2 <- ggboxplot(dat.1, x = "randomization_arm", y = "Abundance", outlier.shape = NA) +
  geom_jitter(width = 0.2) +
  labs(title = "Figure S3", y = "Relative Abundance", x = "") +
  coord_cartesian(ylim = c(0, 1.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(comparisons = my_comparisons.1, label = "p.format", hide.ns = TRUE) +
  facet_grid(day~Phylum)
p.boxplot.phylum.2

# Note: For the figure in the manuscript we manually replaced the asterisks and removed the ns for aesthetic purposes

# Not inlcuded in the final manuscript, but referenced in the text
p.boxplot.phylum.3 <- ggpaired(dat.1, x = "day", y = "Abundance", outlier.shape = NA, id = "patient_ID") +
  geom_jitter(width = 0.2) +
  labs(y = "Relative Abundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(comparisons = my_comparisons.2, hide.ns = FALSE, label = "p.signif") +
  facet_grid(randomization_arm~Phylum) +
  coord_cartesian(ylim = c(0, 1.5))
p.boxplot.phylum.3

# Reorder for legend consistency
# Reorder
levels(dat.1$Phylum)
## [1] "Fusobacteria"    "Actinobacteria"  "Verrucomicrobia" "Proteobacteria" 
## [5] "Bacteroidetes"   "Firmicutes"
dat.1.reorder <- dat.1 %>%
  mutate(Phylum = reorder(Phylum, Abundance, function(x) -mean(x)))
levels(dat.1.reorder$Phylum)
## [1] "Firmicutes"      "Bacteroidetes"   "Proteobacteria"  "Verrucomicrobia"
## [5] "Actinobacteria"  "Fusobacteria"
p.gam.phylum <- ggplot(dat.1.reorder, aes(x = day, y = Abundance, color = Phylum)) +
  stat_smooth(method = "gam", formula = y ~ s(x, bs = "cr", k = 3)) +
  labs(y = "Relative Abundance", x = "Day") +
  scale_x_continuous(breaks = c(-9,0,7)) +
  coord_cartesian(ylim = c(0, 1)) +
  geom_jitter(size = 3, alpha = 0.4, width = 1.5) +
  facet_grid(~randomization_arm)
p.gam.phylum

##Alpha diversity

# Diversity calculations
diversity <- global(ps1)
head(diversity)
##                     richness_0 richness_20 richness_50 richness_80
## 20179.VanessaHarris        129         129         129         129
## 20183.VanessaHarris        107         107         107         107
## 20184.VanessaHarris        122         122         122         122
## 20185.VanessaHarris        167         167         167         167
## 20190.VanessaHarris        178         178         178         178
## 20191.VanessaHarris        101         101         101         101
##                     observed diversities_inverse_simpson
## 20179.VanessaHarris      129                   25.051833
## 20183.VanessaHarris      107                   11.052659
## 20184.VanessaHarris      122                   21.067032
## 20185.VanessaHarris      167                   11.979071
## 20190.VanessaHarris      178                    7.402668
## 20191.VanessaHarris      101                   29.246391
##                     diversities_gini_simpson diversities_shannon
## 20179.VanessaHarris                0.9600828            3.841603
## 20183.VanessaHarris                0.9095240            3.189909
## 20184.VanessaHarris                0.9525325            3.635530
## 20185.VanessaHarris                0.9165211            3.560559
## 20190.VanessaHarris                0.8649136            3.296459
## 20191.VanessaHarris                0.9658077            3.851619
##                     diversities_fisher diversities_coverage
## 20179.VanessaHarris           18.39179                    9
## 20183.VanessaHarris           13.28609                    4
## 20184.VanessaHarris           17.10378                    7
## 20185.VanessaHarris           21.72897                    6
## 20190.VanessaHarris           23.54276                    4
## 20191.VanessaHarris           17.34501                   11
##                     evenness_camargo evenness_pielou evenness_simpson
## 20179.VanessaHarris       0.02411088       0.7904838      0.015775713
## 20183.VanessaHarris       0.01386747       0.6826504      0.006960113
## 20184.VanessaHarris       0.01954583       0.7567682      0.013266393
## 20185.VanessaHarris       0.02284719       0.6956942      0.007543496
## 20190.VanessaHarris       0.02084059       0.6361630      0.004661630
## 20191.VanessaHarris       0.02287182       0.8345651      0.018417123
##                     evenness_evar evenness_bulla dominance_dbp
## 20179.VanessaHarris     0.3119453     0.07638109     0.1067287
## 20183.VanessaHarris     0.2166349     0.05688918     0.2324156
## 20184.VanessaHarris     0.2539446     0.06913749     0.1180977
## 20185.VanessaHarris     0.2327965     0.08566605     0.2434588
## 20190.VanessaHarris     0.2584905     0.09132655     0.3468711
## 20191.VanessaHarris     0.3542731     0.06214156     0.1007699
##                     dominance_dmn dominance_absolute dominance_relative
## 20179.VanessaHarris     0.2032787               2181          0.1067287
## 20183.VanessaHarris     0.3572660               9708          0.2324156
## 20184.VanessaHarris     0.1924694               2528          0.1180977
## 20185.VanessaHarris     0.3570235              11510          0.2434588
## 20190.VanessaHarris     0.4207092              15681          0.3468711
## 20191.VanessaHarris     0.1656116                589          0.1007699
##                     dominance_simpson dominance_core_abundance
## 20179.VanessaHarris        0.03991724                0.4406166
## 20183.VanessaHarris        0.09047596                0.2650946
## 20184.VanessaHarris        0.04746753                0.4612725
## 20185.VanessaHarris        0.08347893                0.2748482
## 20190.VanessaHarris        0.13508643                0.4270356
## 20191.VanessaHarris        0.03419225                0.3223268
##                     dominance_gini rarity_log_modulo_skewness
## 20179.VanessaHarris      0.9758891                   2.060662
## 20183.VanessaHarris      0.9861325                   2.059361
## 20184.VanessaHarris      0.9804542                   2.057755
## 20185.VanessaHarris      0.9771528                   2.060823
## 20190.VanessaHarris      0.9791594                   2.060939
## 20191.VanessaHarris      0.9771282                   2.061423
##                     rarity_low_abundance rarity_noncore_abundance
## 20179.VanessaHarris           0.05784194                0.2032787
## 20183.VanessaHarris           0.03243955                0.2483361
## 20184.VanessaHarris           0.05713351                0.2874895
## 20185.VanessaHarris           0.07466633                0.1344628
## 20190.VanessaHarris           0.07226757                0.1914969
## 20191.VanessaHarris           0.03695466                0.2249786
##                     rarity_rare_abundance
## 20179.VanessaHarris             0.2032787
## 20183.VanessaHarris             0.2483361
## 20184.VanessaHarris             0.2874895
## 20185.VanessaHarris             0.1344628
## 20190.VanessaHarris             0.1914969
## 20191.VanessaHarris             0.2249786
# Bind sample data to diversity data
sd.1 <- as.data.frame(sample_data(ps1)) # useful to coerce the phyloseq object into an R data frame
ps1.rich <- merge(sd.1, diversity, by ="row.names") # merge sd.1 by row names
my.comparisons.paired <- list(c("Pre", "0"), c("Pre", "7"), c("0","7"))

p.paired.rich.1 <- ggpaired(ps1.rich, x = "day", y = "richness_0", outlier.shape = NA, id = "patient_ID") +
  geom_jitter(width = 0.2) +
  labs(y = "Richness") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(~randomization_arm) +
  stat_compare_means(label = "p.signif", method = "t.test", ref.group = "-9", hide.ns = TRUE) +
  theme(axis.text.x = element_blank())

p.paired.sd.1 <- ggpaired(ps1.rich, x = "day", y = "diversities_shannon", outlier.shape = NA, id = "patient_ID") +
  geom_jitter(width = 0.2) +
  labs(y = "Shannon Diversity") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(~randomization_arm) +
  stat_compare_means(label = "p.signif", method = "t.test", ref.group = "-9", hide.ns = TRUE)

ggarrange(p.paired.rich.1, p.paired.sd.1, nrow = 2, labels = c("A)", "B)"))

# Alpha diversity comparisons between treatments
p.paired.rich.2 <- ggboxplot(ps1.rich, x = "randomization_arm", y = "richness_0", outlier.shape = NA, id = "patient_ID") +
  geom_jitter(width = 0.2) +
  labs(y = "Richness") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(~day) +
  stat_compare_means(label = "p.format", comparisons = my_comparisons.1, hide.ns = TRUE) +
  theme(axis.text.x = element_blank())

p.paired.sd.2 <- ggboxplot(ps1.rich, x = "randomization_arm", y = "diversities_shannon", outlier.shape = NA, id = "patient_ID") +
  geom_jitter(width = 0.2) +
  labs(y = "Shannon Diversity") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(~day) +
  stat_compare_means(label = "p.format", comparisons = my_comparisons.1, hide.ns = TRUE)

ggarrange(p.paired.rich.2, p.paired.sd.2, nrow = 2, labels = c("A)", "B)"))

# Note: This figure was not included in the final manuscript, but the analysis and p-values were referenced in the text
# These plots were not inlcuded in the final manuscript, but were discussed in the results
p.rich.rota_boost <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "d7_rota_boost_updated", y = "richness_0", outlier.shape = NA) +
  geom_jitter(width = 0.2) +
  ylim(0,250) +
  labs(y = "Richness", title = "Boost") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
  facet_grid(~day) +
  theme(axis.text.x = element_blank())

p.sd.rota_boost <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "d7_rota_boost_updated", y = "diversities_shannon", outlier.shape = NA) +
  geom_jitter(width = 0.2) +
  ylim(0,5) +
  labs(y = "Shannon diversity") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
  facet_grid(~day)

ggarrange(p.rich.rota_boost, p.sd.rota_boost, nrow = 2, labels = c("A)", "B)"))

# Note: This figure was not included in the final manuscript, but the analysis and p-values were referenced in the text

Alpha diversity and shedding

Shedding was defined as having one or more stool samples per patient positive for rotavirus shedding.

p.rich.shedding <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "Shedding", y = "richness_0", outlier.shape = NA) +
  geom_jitter(width = 0.2) +
  ylim(0,250) +
  labs(y = "Richness", title = "Shedding") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
  facet_grid(~day) +
  theme(axis.text.x = element_blank())

p.sd.shedding <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "Shedding", y = "diversities_shannon", outlier.shape = NA) +
  geom_jitter(width = 0.2) +
  ylim(0,5) +
  labs(y = "Shannon diversity") +
  theme(axis.title.x = element_blank()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
  #facet_grid(~randomization_arm) +
  scale_x_discrete(labels = c("No", "Yes"))

ggarrange(p.rich.shedding, p.sd.shedding, nrow = 2, labels = c("A)", "B)"))

# Note: This figure was not included in the final manuscript, but the analysis and p-values were referenced in the text

Beta diversity analysis

# UniFrac
ord.ps1.uni <- ordinate(ps1, method = "PCoA", distance = "unifrac")

# weighted UniFrac
ord.ps1.wuni <- ordinate(ps1, method = "PCoA", distance = "wunifrac")

# PCoA plot - Boost
p.ord.wuni <- plot_ordination(ps1, ord.ps1.wuni, color = "randomization_arm", shape = "d7_rota_boost_updated") +
  geom_point(size=3, alpha = 0.7) +
  scale_color_brewer(palette = "Dark2") +
  geom_point(colour = "grey50", size = 0.5) +
  facet_grid(~ day) +
  labs(color = "Treatment", shape = "Day 7 Rotavirus Boosted") +
  theme(legend.position = "null") +
  geom_hline(yintercept = 0, size = 0.1, lty = 2) +
  geom_vline(xintercept = 0, size = 0.1, lty = 2) +
  theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor = element_blank())

p.ord.uni <- plot_ordination(ps1, ord.ps1.uni, color = "randomization_arm", shape = "d7_rota_boost_updated") +
  geom_point(size=3, alpha = 0.7) +
  scale_color_brewer(palette = "Dark2") +
  geom_point(colour = "grey50", size = 0.5) +
  facet_grid(~ day) +
  labs(color = "Treatment", shape = "Rotavirus Boosted") +
  theme(legend.position = "null")+
  geom_hline(yintercept = 0, size = 0.1, lty = 2) +
  geom_vline(xintercept = 0, size = 0.1, lty = 2) +
  theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor = element_blank())

ggarrange(p.ord.uni, p.ord.wuni, nrow = 2)

# Species ordination for all Phylum
# Note: The final manuscript just showed the top 6 most abundant phylum. Code for this is in the following chunk
plot_ordination(ps1, ord.ps1.wuni, color = "Genus", type = "taxa") +
  geom_point(size=2.5) +
  geom_point(colour = "grey50", size = 0.25) +
  labs(color = "Day 7 Rotavirus Boosted", shape = "Treatment") +
  theme(legend.position = "null") +
  facet_wrap(~Phylum, ncol = 4) +
  geom_hline(yintercept = 0, size = 0.1, lty = 2) +
  geom_vline(xintercept = 0, size = 0.1, lty = 2) +
  theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor = element_blank())

## Beta diversity with top 6 Phyla

# Select only top phyla for display purposes
phylum.sum <- tapply(taxa_sums(ps1), tax_table(ps1)[, "Phylum"], sum, na.rm=TRUE)

# Select top 6 most abundant phyla (to correspond with other figrures)
top6phyla <- names(sort(phylum.sum, TRUE))[1:6]
top6phyla
## [1] "Firmicutes"      "Bacteroidetes"   "Proteobacteria"  "Verrucomicrobia"
## [5] "Fusobacteria"    "Actinobacteria"
ps1.top6 <- prune_taxa((tax_table(ps1)[, "Phylum"] %in% top6phyla), ps1)
get_taxa_unique(ps1.top6, "Phylum")
## [1] "Bacteroidetes"   "Firmicutes"      "Verrucomicrobia" "Proteobacteria" 
## [5] "Fusobacteria"    "Actinobacteria"
# UniFrac
ord.ps1.uni.top6 <- ordinate(ps1.top6, method = "PCoA", distance = "unifrac")

# weighted UniFrac
ord.ps1.wuni.top6 <- ordinate(ps1.top6, method = "PCoA", distance = "wunifrac")

p.ord.species <- plot_ordination(ps1.top6, ord.ps1.wuni, color = "Genus", type = "taxa") +
  geom_point(size=2.5) +
  geom_point(colour = "grey50", size = 0.25) +
  labs(color = "Day 7 Rotavirus Boosted", shape = "Treatment") +
  theme(legend.position = "null") +
  facet_wrap(~Phylum, ncol = 6) +
  geom_hline(yintercept = 0, size = 0.1, lty = 2) +
  geom_vline(xintercept = 0, size = 0.1, lty = 2) +
  theme(panel.grid.major = element_blank()) +
  theme(panel.grid.minor = element_blank())
p.ord.species

ggarrange(p.ord.uni, p.ord.wuni, p.ord.species, nrow = 3, labels = c("A)", "B)", "C)"))

# Note: p.ord.species was re-ordered in Keynote because SH couldn't figure out a simple way to reorder the phyla in PhyloSeq. He is sure there is a more sophisticated way to do this though :)

Premanova significance testing (ADONIS)

# Create relevant subsets
ps1.d_9 <- subset_samples(ps1, day == "-9")
ps1.d0 <- subset_samples(ps1, day == "0")
ps1.d7 <- subset_samples(ps1, day == "7")

# Day -9
ps1_otu_table.d_9 <- as.data.frame(otu_table(ps1.d_9))
sd.df.d_9 <- as.data.frame(sample_data(ps1.d_9))
ps1_otu_table.d_9$randomization_arm <- sd.df.d_9$randomization_arm

kable(pairwise.adonis(ps1_otu_table.d_9[,1:1588], ps1_otu_table.d_9$randomization_arm), format = "pandoc", caption = "Day -9")
Day -9
pairs total.DF F.Model R2 p.value p.adjusted sig
No antibiotics vs Narrow spectrum antibiotics 16 0.6982874 0.0444818 0.911 1
No antibiotics vs Broad spectrum antibiotics 15 1.0235432 0.0681293 0.396 1
Narrow spectrum antibiotics vs Broad spectrum antibiotics 14 0.7760274 0.0563317 0.840 1
# Day 0
ps1_otu_table.d0 <- as.data.frame(otu_table(ps1.d0))
sd.df.d0 <- as.data.frame(sample_data(ps1.d0))
ps1_otu_table.d0$randomization_arm <- sd.df.d0$randomization_arm

kable(pairwise.adonis(ps1_otu_table.d0[,1:1588], ps1_otu_table.d0$randomization_arm), format = "pandoc", caption = "Day 0")
Day 0
pairs total.DF F.Model R2 p.value p.adjusted sig
Narrow spectrum antibiotics vs Broad spectrum antibiotics 22 3.115567 0.1291932 0.001 0.003 *
Narrow spectrum antibiotics vs No antibiotics 23 5.332591 0.1951001 0.001 0.003 *
Broad spectrum antibiotics vs No antibiotics 20 3.449463 0.1536546 0.001 0.003 *
# Day 7
ps1_otu_table.d7 <- as.data.frame(otu_table(ps1.d7))
sd.df.d7 <- as.data.frame(sample_data(ps1.d7))
ps1_otu_table.d7$randomization_arm <- sd.df.d7$randomization_arm

kable(pairwise.adonis(ps1_otu_table.d7[,1:1588], ps1_otu_table.d7$randomization_arm), format = "pandoc", caption = "Day 7")
Day 7
pairs total.DF F.Model R2 p.value p.adjusted sig
Narrow spectrum antibiotics vs Broad spectrum antibiotics 27 1.270326 0.0465827 0.074 0.222
Narrow spectrum antibiotics vs No antibiotics 28 1.570740 0.0549772 0.031 0.093
Broad spectrum antibiotics vs No antibiotics 26 1.716849 0.0642609 0.002 0.006 *
#Differential Abundance Testing

Pairwise comparisons across treatments at each sampling time

Differential abundance testing preperation

# Set alpha
alpha = 0.05

# Calculate geometric means prior to estimate size factors
# This is required for data sets with lots of zeros
gm_mean = function(x, na.rm=TRUE){
  exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}

# Generate subsets
# Pairwise treatment groups
ps1.CvN <- subset_samples(ps1, randomization_arm != "Broad spectrum antibiotics")
sample_data(ps1.CvN)$randomization_arm
##  [1] No antibiotics              No antibiotics             
##  [3] No antibiotics              No antibiotics             
##  [5] Narrow spectrum antibiotics No antibiotics             
##  [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [9] No antibiotics              Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics No antibiotics             
## [13] Narrow spectrum antibiotics No antibiotics             
## [15] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [17] No antibiotics              Narrow spectrum antibiotics
## [19] No antibiotics              No antibiotics             
## [21] Narrow spectrum antibiotics No antibiotics             
## [23] No antibiotics              Narrow spectrum antibiotics
## [25] Narrow spectrum antibiotics No antibiotics             
## [27] Narrow spectrum antibiotics No antibiotics             
## [29] No antibiotics              Narrow spectrum antibiotics
## [31] No antibiotics              Narrow spectrum antibiotics
## [33] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [35] Narrow spectrum antibiotics No antibiotics             
## [37] Narrow spectrum antibiotics No antibiotics             
## [39] Narrow spectrum antibiotics No antibiotics             
## [41] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [43] No antibiotics              Narrow spectrum antibiotics
## [45] No antibiotics              No antibiotics             
## [47] Narrow spectrum antibiotics No antibiotics             
## [49] No antibiotics              No antibiotics             
## [51] No antibiotics              Narrow spectrum antibiotics
## [53] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [55] No antibiotics              Narrow spectrum antibiotics
## [57] Narrow spectrum antibiotics No antibiotics             
## [59] Narrow spectrum antibiotics No antibiotics             
## [61] No antibiotics              Narrow spectrum antibiotics
## [63] No antibiotics              Narrow spectrum antibiotics
## [65] Narrow spectrum antibiotics No antibiotics             
## [67] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [69] No antibiotics              Narrow spectrum antibiotics
## Levels: No antibiotics Narrow spectrum antibiotics
ps1.CvB <- subset_samples(ps1, randomization_arm != "Narrow spectrum antibiotics")
sample_data(ps1.CvB)$randomization_arm
##  [1] No antibiotics             No antibiotics            
##  [3] No antibiotics             No antibiotics            
##  [5] No antibiotics             Broad spectrum antibiotics
##  [7] No antibiotics             Broad spectrum antibiotics
##  [9] Broad spectrum antibiotics Broad spectrum antibiotics
## [11] No antibiotics             No antibiotics            
## [13] Broad spectrum antibiotics Broad spectrum antibiotics
## [15] No antibiotics             Broad spectrum antibiotics
## [17] Broad spectrum antibiotics Broad spectrum antibiotics
## [19] No antibiotics             No antibiotics            
## [21] Broad spectrum antibiotics No antibiotics            
## [23] No antibiotics             No antibiotics            
## [25] Broad spectrum antibiotics Broad spectrum antibiotics
## [27] No antibiotics             No antibiotics            
## [29] No antibiotics             Broad spectrum antibiotics
## [31] Broad spectrum antibiotics No antibiotics            
## [33] Broad spectrum antibiotics Broad spectrum antibiotics
## [35] Broad spectrum antibiotics No antibiotics            
## [37] No antibiotics             Broad spectrum antibiotics
## [39] Broad spectrum antibiotics No antibiotics            
## [41] Broad spectrum antibiotics No antibiotics            
## [43] No antibiotics             Broad spectrum antibiotics
## [45] No antibiotics             No antibiotics            
## [47] Broad spectrum antibiotics Broad spectrum antibiotics
## [49] No antibiotics             No antibiotics            
## [51] No antibiotics             Broad spectrum antibiotics
## [53] No antibiotics             No antibiotics            
## [55] No antibiotics             Broad spectrum antibiotics
## [57] No antibiotics             Broad spectrum antibiotics
## [59] Broad spectrum antibiotics No antibiotics            
## [61] Broad spectrum antibiotics Broad spectrum antibiotics
## [63] No antibiotics             Broad spectrum antibiotics
## Levels: No antibiotics Broad spectrum antibiotics
ps1.NvB <- subset_samples(ps1, randomization_arm != "No antibiotics")
sample_data(ps1.NvB)$randomization_arm
##  [1] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [3] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [5] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [7] Broad spectrum antibiotics  Broad spectrum antibiotics 
##  [9] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [13] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [15] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [17] Broad spectrum antibiotics  Broad spectrum antibiotics 
## [19] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [21] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [23] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [25] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [27] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [29] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [31] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [33] Broad spectrum antibiotics  Broad spectrum antibiotics 
## [35] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [37] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [39] Broad spectrum antibiotics  Broad spectrum antibiotics 
## [41] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [43] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [45] Broad spectrum antibiotics  Broad spectrum antibiotics 
## [47] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [49] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [51] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [53] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [55] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [57] Broad spectrum antibiotics  Broad spectrum antibiotics 
## [59] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [61] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [63] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [65] Narrow spectrum antibiotics Broad spectrum antibiotics 
## Levels: Narrow spectrum antibiotics Broad spectrum antibiotics
# Day subsets
ps1.CvN
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 70 samples ]
## sample_data() Sample Data:       [ 70 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
ps1.CvN_9 <- subset_samples(ps1.CvN, day == "-9")
ps1.CvN_9
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 17 samples ]
## sample_data() Sample Data:       [ 17 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.CvN_9)$day
##  [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9
ps1.CvN.0 <- subset_samples(ps1.CvN, day == "0")
ps1.CvN.0
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 24 samples ]
## sample_data() Sample Data:       [ 24 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.CvN.0)$day
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ps1.CvN.7 <- subset_samples(ps1.CvN, day == "7")
ps1.CvN.7
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 29 samples ]
## sample_data() Sample Data:       [ 29 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.CvN.7)$day
##  [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
# Control vs Broad by day
ps1.CvB_9 <- subset_samples(ps1.CvB, day == "-9")
ps1.CvB_9
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 16 samples ]
## sample_data() Sample Data:       [ 16 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.CvB_9)$day
##  [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9
ps1.CvB.0 <- subset_samples(ps1.CvB, day == "0")
ps1.CvB.0
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 21 samples ]
## sample_data() Sample Data:       [ 21 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.CvB.0)$day
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ps1.CvB.7 <- subset_samples(ps1.CvB, day == "7")
ps1.CvB.7
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 27 samples ]
## sample_data() Sample Data:       [ 27 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.CvB.7)$day
##  [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
# Narrow vs Broad by day
ps1.NvB_9 <- subset_samples(ps1.NvB, day == "-9")
ps1.NvB_9
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 15 samples ]
## sample_data() Sample Data:       [ 15 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.NvB_9)$day
##  [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9
ps1.NvB.0 <- subset_samples(ps1.NvB, day == "0")
ps1.NvB.0
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 23 samples ]
## sample_data() Sample Data:       [ 23 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.NvB.0)$day
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ps1.NvB.7 <- subset_samples(ps1.NvB, day == "7")
ps1.NvB.7
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 1588 taxa and 28 samples ]
## sample_data() Sample Data:       [ 28 samples by 120 sample variables ]
## tax_table()   Taxonomy Table:    [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree()    Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]
sample_data(ps1.NvB.7)$day
##  [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

Control vs Narrow Spectrum Day -9

# Differential Abundance Testing
sample_data(ps1.CvN_9)$randomization_arm
##  [1] No antibiotics              No antibiotics             
##  [3] No antibiotics              No antibiotics             
##  [5] Narrow spectrum antibiotics No antibiotics             
##  [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [9] No antibiotics              Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics No antibiotics             
## [13] Narrow spectrum antibiotics No antibiotics             
## [15] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [17] No antibiotics             
## Levels: No antibiotics Narrow spectrum antibiotics
sample_data(ps1.CvN_9)$day
##  [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9
ds.NoAbx.Narrow.d_9 <- phyloseq_to_deseq2(ps1.CvN_9, ~randomization_arm)

geoMeans.ds.NoAbx.Narrow.d_9 <- apply(counts(ds.NoAbx.Narrow.d_9), 1, gm_mean)
ds.NoAbx.Narrow.d_9 <- estimateSizeFactors(ds.NoAbx.Narrow.d_9, geoMeans = geoMeans.ds.NoAbx.Narrow.d_9)
dds.NoAbx.Narrow.d_9 <- DESeq(ds.NoAbx.Narrow.d_9, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.NoAbx.Narrow.d_9 = results(dds.NoAbx.Narrow.d_9, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Narrow.d_9 = res.dds.NoAbx.Narrow.d_9[which(res.dds.NoAbx.Narrow.d_9$padj < alpha), ]
sigtab_dds.dds.NoAbx.Narrow.d_9 = cbind(as(sigtab_dds.dds.NoAbx.Narrow.d_9, "data.frame"), as(tax_table(ps1.CvN_9)[rownames(sigtab_dds.dds.NoAbx.Narrow.d_9), ], "matrix"))
summary(res.dds.NoAbx.Narrow.d_9)
## 
## out of 436 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 4, 0.92%
## LFC < 0 (down)     : 2, 0.46%
## outliers [1]       : 0, 0%
## low counts [2]     : 216, 50%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.NoAbx.Narrow.d_9)
##                                                                                                                                                                                                                                            baseMean
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG   6.380570
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 19.804627
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 14.018301
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 25.176158
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT  8.690063
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 13.950333
##                                                                                                                                                                                                                                           log2FoldChange
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG        21.25830
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT       22.82752
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT       22.35159
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT       22.76903
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT      -22.07018
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT      -20.82741
##                                                                                                                                                                                                                                              lfcSE
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  2.972809
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.970430
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 2.970899
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 2.970189
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 2.981681
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 2.980680
##                                                                                                                                                                                                                                                stat
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG   7.150914
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT  7.684921
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT  7.523511
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT  7.665852
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT -7.401924
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT -6.987471
##                                                                                                                                                                                                                                                 pvalue
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  8.620211e-13
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 1.530915e-14
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 5.332451e-14
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 1.776479e-14
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 1.342258e-13
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 2.798857e-12
##                                                                                                                                                                                                                                                   padj
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  7.516824e-11
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 3.872725e-12
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 7.749829e-12
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 3.872725e-12
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 1.463061e-11
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 2.033836e-10
##                                                                                                                                                                                                                                            Kingdom
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteria
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Bacteria
##                                                                                                                                                                                                                                                  Phylum
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG     Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT    Firmicutes
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidetes
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidetes
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Bacteroidetes
##                                                                                                                                                                                                                                                   Class
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  Negativicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidia
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT       Bacilli
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT   Bacteroidia
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT   Bacteroidia
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT   Bacteroidia
##                                                                                                                                                                                                                                                     Order
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  Selenomonadales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidales
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillales
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT   Bacteroidales
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT   Bacteroidales
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT   Bacteroidales
##                                                                                                                                                                                                                                                       Family
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG     Veillonellaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT     Prevotellaceae
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT   Lactobacillaceae
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT     Bacteroidaceae
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT     Bacteroidaceae
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Porphyromonadaceae
##                                                                                                                                                                                                                                                   Genus
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG      Dialister
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT    Prevotella
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillus
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT   Bacteroides
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT   Bacteroides
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT   Barnesiella
##                                                                                                                                                                                                                                           Species
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG     <NA>
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT    <NA>
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT ruminis
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT    <NA>
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT    <NA>
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT    <NA>
write.table(sigtab_dds.dds.NoAbx.Narrow.d_9, file="./results/deseq_d_9_NoAbx.Narrow.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.NoAbx.Narrow.d_9, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                            description
##                                                                                            <character>
## baseMean                                                     mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj                                                                              BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Narrow.d_9 = data.table(as(results(dds.NoAbx.Narrow.d_9, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Narrow.d_9, "rn", "OTU")
taxdt.dds.d_9.com = data.table(data.frame(as(tax_table(ps1.CvN_9), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d_9.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d_9.com, "OTU")
setkeyv(resdt.dds.NoAbx.Narrow.d_9, "OTU")
resdt.dds.NoAbx.Narrow.d_9 <- taxdt.dds.d_9.com[resdt.dds.NoAbx.Narrow.d_9]
resdt.dds.NoAbx.Narrow.d_9
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.4588114      -2.302210
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.2117591      -1.473163
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000       0.000000
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000       0.000000
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000       0.000000
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.0000000             NA
##          lfcSE       stat    pvalue      padj
##    1: 3.024684 -0.7611405 0.4465731 0.9612434
##    2:       NA         NA        NA        NA
##    3:       NA         NA        NA        NA
##    4: 3.042592 -0.4841803 0.6282579 0.9612434
##    5:       NA         NA        NA        NA
##   ---                                        
## 1584: 0.000000  0.0000000 1.0000000        NA
## 1585: 0.000000  0.0000000 1.0000000        NA
## 1586: 0.000000  0.0000000 1.0000000        NA
## 1587:       NA         NA        NA        NA
## 1588:       NA         NA        NA        NA
resdt.dds.NoAbx.Narrow.d_9[, Significant := padj < alpha]
resdt.dds.NoAbx.Narrow.d_9[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTTTTCTTGAGTGCTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGACAGTAACTGACGTTGAGGCACGAAAGCGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 432:  GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGTCAACTGGACGATAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 433: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 434: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 435: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 436: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 432: Bacteria      Firmicutes       Clostridia      Clostridiales
## 433: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 434: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 435: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 436: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
##                     Family            Genus     Species     baseMean
##   1:       Ruminococcaceae             <NA>        <NA>    0.4588114
##   2:         Rikenellaceae             <NA>        <NA>    0.2117591
##   3:       Ruminococcaceae Faecalibacterium        <NA>    0.2566115
##   4:       Ruminococcaceae Faecalibacterium prausnitzii 1725.8062256
##   5:       Ruminococcaceae Faecalibacterium        <NA>    6.8274327
##  ---                                                                
## 432: Peptostreptococcaceae Terrisporobacter        <NA>    0.9902423
## 433:         Rikenellaceae        Alistipes        <NA>   27.0876943
## 434:         Rikenellaceae        Alistipes  putredinis  199.1970531
## 435:   Verrucomicrobiaceae      Akkermansia muciniphila  154.5876656
## 436:        Bacteroidaceae      Bacteroides        <NA>    8.6900633
##      log2FoldChange    lfcSE       stat       pvalue         padj
##   1:     -2.3022098 3.024684 -0.7611405 4.465731e-01 9.612434e-01
##   2:     -1.4731633 3.042592 -0.4841803 6.282579e-01 9.612434e-01
##   3:      1.6990947 3.037164  0.5594347 5.758651e-01 9.612434e-01
##   4:      0.9345633 1.158837  0.8064665 4.199739e-01 9.612434e-01
##   5:      6.3025439 2.972589  2.1202204 3.398746e-02 9.612434e-01
##  ---                                                             
## 432:     -3.2987529 3.002899 -1.0985228 2.719763e-01 9.612434e-01
## 433:      1.7229974 2.057375  0.8374737 4.023263e-01 9.612434e-01
## 434:     -0.8036767 1.245101 -0.6454710 5.186220e-01 9.612434e-01
## 435:     -3.1964282 1.982376 -1.6124226 1.068700e-01 9.612434e-01
## 436:    -22.0701779 2.981681 -7.4019235 1.342258e-13 1.463061e-11
##      Significant
##   1:       FALSE
##   2:       FALSE
##   3:       FALSE
##   4:       FALSE
##   5:       FALSE
##  ---            
## 432:       FALSE
## 433:       FALSE
## 434:       FALSE
## 435:       FALSE
## 436:        TRUE
resdt.dds.NoAbx.Narrow.d_9
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.4588114      -2.302210
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.2117591      -1.473163
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000       0.000000
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000       0.000000
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000       0.000000
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.0000000             NA
##          lfcSE       stat    pvalue      padj Significant
##    1: 3.024684 -0.7611405 0.4465731 0.9612434       FALSE
##    2:       NA         NA        NA        NA          NA
##    3:       NA         NA        NA        NA          NA
##    4: 3.042592 -0.4841803 0.6282579 0.9612434       FALSE
##    5:       NA         NA        NA        NA          NA
##   ---                                                    
## 1584: 0.000000  0.0000000 1.0000000        NA          NA
## 1585: 0.000000  0.0000000 1.0000000        NA          NA
## 1586: 0.000000  0.0000000 1.0000000        NA          NA
## 1587:       NA         NA        NA        NA          NA
## 1588:       NA         NA        NA        NA          NA
volcano.NoAbx.Narrow.d_9 = ggplot(
  data = resdt.dds.NoAbx.Narrow.d_9[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.NoAbx.Narrow.d_9[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay -9 No Antibiotics vs Narrow Spectrum Antibiotics") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Narrow.d_9

summary(res.dds.NoAbx.Narrow.d_9)
## 
## out of 436 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 4, 0.92%
## LFC < 0 (down)     : 2, 0.46%
## outliers [1]       : 0, 0%
## low counts [2]     : 216, 50%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.NoAbx.Narrow.d_9, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                            description
##                                                                                            <character>
## baseMean                                                     mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj                                                                              BH adjusted p-values
ggplotly(volcano.NoAbx.Narrow.d_9)

##Control vs Narrow Spectrum Day 0

# Differential Abundance Testing
sample_data(ps1.CvN.0)$randomization_arm
##  [1] Narrow spectrum antibiotics No antibiotics             
##  [3] No antibiotics              No antibiotics             
##  [5] No antibiotics              Narrow spectrum antibiotics
##  [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [9] No antibiotics              Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics No antibiotics             
## [13] Narrow spectrum antibiotics No antibiotics             
## [15] No antibiotics              Narrow spectrum antibiotics
## [17] No antibiotics              Narrow spectrum antibiotics
## [19] Narrow spectrum antibiotics No antibiotics             
## [21] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [23] No antibiotics              Narrow spectrum antibiotics
## Levels: No antibiotics Narrow spectrum antibiotics
sample_data(ps1.CvN.0)$day
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ds.NoAbx.Narrow.d0 <- phyloseq_to_deseq2(ps1.CvN.0, ~randomization_arm)

geoMeans.ds.NoAbx.Narrow.d0 <- apply(counts(ds.NoAbx.Narrow.d0), 1, gm_mean)
ds.NoAbx.Narrow.d0 <- estimateSizeFactors(ds.NoAbx.Narrow.d0, geoMeans = geoMeans.ds.NoAbx.Narrow.d0)

dds.NoAbx.Narrow.d0 <- DESeq(ds.NoAbx.Narrow.d0, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.NoAbx.Narrow.d0 = results(dds.NoAbx.Narrow.d0, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Narrow.d0 = res.dds.NoAbx.Narrow.d0[which(res.dds.NoAbx.Narrow.d0$padj < alpha), ]
sigtab_dds.dds.NoAbx.Narrow.d0 = cbind(as(sigtab_dds.dds.NoAbx.Narrow.d0, "data.frame"), as(tax_table(ps1.CvN_9)[rownames(sigtab_dds.dds.NoAbx.Narrow.d0), ], "matrix"))
summary(res.dds.NoAbx.Narrow.d0)
## 
## out of 410 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 18, 4.4%
## LFC < 0 (down)     : 122, 30%
## outliers [1]       : 0, 0%
## low counts [2]     : 492, 120%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.NoAbx.Narrow.d0)
##                                                                                                                                                                                                                                            baseMean
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  493.9877
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  712.2997
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT  110.1263
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT  437.4761
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1716.4159
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  273.5243
##                                                                                                                                                                                                                                           log2FoldChange
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      -9.226542
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     -13.138562
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT      -6.373276
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      -4.208895
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG       9.393029
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      -7.097995
##                                                                                                                                                                                                                                              lfcSE
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.703820
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.247507
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.906295
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.690638
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.104618
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.662932
##                                                                                                                                                                                                                                                 stat
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  -5.415210
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -10.531855
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT  -2.192921
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT  -2.489531
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG   8.503419
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  -4.268362
##                                                                                                                                                                                                                                                 pvalue
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 6.121676e-08
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 6.160853e-26
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.831304e-02
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.279118e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.840869e-17
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.969133e-05
##                                                                                                                                                                                                                                                   padj
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.740179e-07
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 5.791202e-24
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 4.710488e-02
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.404743e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 3.460835e-16
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 6.494683e-05
##                                                                                                                                                                                                                                            Kingdom
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
##                                                                                                                                                                                                                                                   Phylum
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT  Bacteroidetes
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT  Bacteroidetes
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Firmicutes
##                                                                                                                                                                                                                                                         Class
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT          Clostridia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT          Clostridia
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT         Bacteroidia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT         Bacteroidia
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT          Clostridia
##                                                                                                                                                                                                                                                       Order
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Clostridiales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Clostridiales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT     Bacteroidales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT     Bacteroidales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Clostridiales
##                                                                                                                                                                                                                                                       Family
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Ruminococcaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Ruminococcaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT     Prevotellaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT     Bacteroidaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Ruminococcaceae
##                                                                                                                                                                                                                                                          Genus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Faecalibacterium
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Faecalibacterium
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT           Prevotella
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT          Bacteroides
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Escherichia/Shigella
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Faecalibacterium
##                                                                                                                                                                                                                                               Species
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT        <NA>
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT        <NA>
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG        <NA>
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
write.table(sigtab_dds.dds.NoAbx.Narrow.d0, file="./results/deseq_d0_NoAbx.Narrow.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.NoAbx.Narrow.d0, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                            description
##                                                                                            <character>
## baseMean                                                     mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj                                                                              BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Narrow.d0 = data.table(as(results(dds.NoAbx.Narrow.d0, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Narrow.d0, "rn", "OTU")
taxdt.dds.d0.com = data.table(data.frame(as(tax_table(ps1.CvN.0), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d0.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d0.com, "OTU")
setkeyv(resdt.dds.NoAbx.Narrow.d0, "OTU")
resdt.dds.NoAbx.Narrow.d0 <- taxdt.dds.d0.com[resdt.dds.NoAbx.Narrow.d0]
resdt.dds.NoAbx.Narrow.d0
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.2373005       1.423492
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000       0.000000
## 1588:   Prevotellaceae    Prevotella     bivia 0.4302047       2.036785
##          lfcSE      stat    pvalue padj
##    1:       NA        NA        NA   NA
##    2:       NA        NA        NA   NA
##    3: 3.000450 0.4744264 0.6351959   NA
##    4:       NA        NA        NA   NA
##    5:       NA        NA        NA   NA
##   ---                                  
## 1584:       NA        NA        NA   NA
## 1585:       NA        NA        NA   NA
## 1586:       NA        NA        NA   NA
## 1587: 0.000000 0.0000000 1.0000000   NA
## 1588: 2.992706 0.6805833 0.4961352   NA
resdt.dds.NoAbx.Narrow.d0[, Significant := padj < alpha]
resdt.dds.NoAbx.Narrow.d0[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 184:  GCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 185:  GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 186: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 187: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 188: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria      Firmicutes       Clostridia      Clostridiales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 184: Bacteria      Firmicutes       Clostridia      Clostridiales
## 185: Bacteria      Firmicutes       Clostridia      Clostridiales
## 186: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 187: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 188: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
##                     Family            Genus     Species   baseMean
##   1:       Ruminococcaceae Faecalibacterium prausnitzii 493.987697
##   2:       Ruminococcaceae Faecalibacterium prausnitzii 712.299690
##   3:       Ruminococcaceae Faecalibacterium prausnitzii 273.524332
##   4:       Ruminococcaceae Faecalibacterium prausnitzii 126.784775
##   5:       Ruminococcaceae Faecalibacterium prausnitzii  94.794736
##  ---                                                              
## 184: Peptostreptococcaceae       Romboutsia        <NA>  36.755315
## 185: Peptostreptococcaceae  Intestinibacter  bartlettii   8.255784
## 186:         Rikenellaceae        Alistipes        <NA>   8.014145
## 187:         Rikenellaceae        Alistipes  putredinis  72.556359
## 188:   Verrucomicrobiaceae      Akkermansia muciniphila 982.106534
##      log2FoldChange    lfcSE        stat       pvalue         padj
##   1:     -9.2265418 1.703820  -5.4152102 6.121676e-08 2.740179e-07
##   2:    -13.1385617 1.247507 -10.5318546 6.160853e-26 5.791202e-24
##   3:     -7.0979951 1.662932  -4.2683624 1.969133e-05 6.494683e-05
##   4:    -26.9168699 2.950364  -9.1232385 7.291230e-20 2.284585e-18
##   5:    -10.2208721 1.842767  -5.5464820 2.914746e-08 1.369931e-07
##  ---                                                              
## 184:      0.3299962 1.614617   0.2043805 8.380562e-01 8.380562e-01
## 185:     -0.8647764 2.904564  -0.2977302 7.659091e-01 7.783293e-01
## 186:     -1.5267737 2.906078  -0.5253726 5.993242e-01 6.156992e-01
## 187:     -6.5553995 1.968679  -3.3298463 8.689394e-04 2.042008e-03
## 188:      3.7576177 2.243088   1.6751984 9.389531e-02 1.209063e-01
##      Significant
##   1:        TRUE
##   2:        TRUE
##   3:        TRUE
##   4:        TRUE
##   5:        TRUE
##  ---            
## 184:       FALSE
## 185:       FALSE
## 186:       FALSE
## 187:        TRUE
## 188:       FALSE
resdt.dds.NoAbx.Narrow.d0
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.2373005       1.423492
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000       0.000000
## 1588:   Prevotellaceae    Prevotella     bivia 0.4302047       2.036785
##          lfcSE      stat    pvalue padj Significant
##    1:       NA        NA        NA   NA          NA
##    2:       NA        NA        NA   NA          NA
##    3: 3.000450 0.4744264 0.6351959   NA          NA
##    4:       NA        NA        NA   NA          NA
##    5:       NA        NA        NA   NA          NA
##   ---                                              
## 1584:       NA        NA        NA   NA          NA
## 1585:       NA        NA        NA   NA          NA
## 1586:       NA        NA        NA   NA          NA
## 1587: 0.000000 0.0000000 1.0000000   NA          NA
## 1588: 2.992706 0.6805833 0.4961352   NA          NA
volcano.NoAbx.Narrow.d0 = ggplot(
  data = resdt.dds.NoAbx.Narrow.d0[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.NoAbx.Narrow.d0[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 0 No Antibiotics vs Narrow Spectrum Antibiotics") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Narrow.d0

summary(res.dds.NoAbx.Narrow.d0)
## 
## out of 410 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 18, 4.4%
## LFC < 0 (down)     : 122, 30%
## outliers [1]       : 0, 0%
## low counts [2]     : 492, 120%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.NoAbx.Narrow.d0, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                            description
##                                                                                            <character>
## baseMean                                                     mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj                                                                              BH adjusted p-values
ggplotly(volcano.NoAbx.Narrow.d0)
nrow(res.dds.NoAbx.Narrow.d0)
## [1] 1588

Control vs Narrow Spectrum Day 7

# Differential Abundance Testing
sample_data(ps1.CvN.7)$randomization_arm
##  [1] Narrow spectrum antibiotics No antibiotics             
##  [3] No antibiotics              Narrow spectrum antibiotics
##  [5] No antibiotics              No antibiotics             
##  [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [9] No antibiotics              Narrow spectrum antibiotics
## [11] No antibiotics              No antibiotics             
## [13] Narrow spectrum antibiotics No antibiotics             
## [15] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [17] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [19] No antibiotics              Narrow spectrum antibiotics
## [21] No antibiotics              Narrow spectrum antibiotics
## [23] No antibiotics              Narrow spectrum antibiotics
## [25] Narrow spectrum antibiotics No antibiotics             
## [27] Narrow spectrum antibiotics No antibiotics             
## [29] No antibiotics             
## Levels: No antibiotics Narrow spectrum antibiotics
sample_data(ps1.CvN.7)$day
##  [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
ds.NoAbx.Narrow.d7 <- phyloseq_to_deseq2(ps1.CvN.7, ~randomization_arm)

geoMeans.ds.NoAbx.Narrow.d7 <- apply(counts(ds.NoAbx.Narrow.d7), 1, gm_mean)
ds.NoAbx.Narrow.d7 <- estimateSizeFactors(ds.NoAbx.Narrow.d7, geoMeans = geoMeans.ds.NoAbx.Narrow.d7)

dds.NoAbx.Narrow.d7 <- DESeq(ds.NoAbx.Narrow.d7, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.NoAbx.Narrow.d7 = results(dds.NoAbx.Narrow.d7, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Narrow.d7 = res.dds.NoAbx.Narrow.d7[which(res.dds.NoAbx.Narrow.d7$padj < alpha), ]
sigtab_dds.dds.NoAbx.Narrow.d7 = cbind(as(sigtab_dds.dds.NoAbx.Narrow.d7, "data.frame"), as(tax_table(ps1.CvN_9)[rownames(sigtab_dds.dds.NoAbx.Narrow.d7), ], "matrix"))
summary(res.dds.NoAbx.Narrow.d7)
## 
## out of 515 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 6, 1.2%
## LFC < 0 (down)     : 7, 1.4%
## outliers [1]       : 0, 0%
## low counts [2]     : 569, 110%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.NoAbx.Narrow.d7)
##                                                                                                                                                                                                                                            baseMean
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 22.252930
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  59.863464
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 14.396651
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT  6.008561
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG  6.705112
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG  7.508508
##                                                                                                                                                                                                                                           log2FoldChange
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG       7.407523
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT       23.888504
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT      22.520806
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT      21.302374
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG     -22.321128
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG     -22.677763
##                                                                                                                                                                                                                                              lfcSE
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 2.309413
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  2.938291
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 2.938839
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 2.939843
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG 2.938088
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 2.937734
##                                                                                                                                                                                                                                                stat
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  3.207535
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT   8.130068
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT  7.663164
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT  7.246093
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG -7.597162
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG -7.719474
##                                                                                                                                                                                                                                                 pvalue
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.338778e-03
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  4.290506e-16
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 1.814076e-14
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 4.289662e-13
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG 3.026955e-14
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 1.168111e-14
##                                                                                                                                                                                                                                                   padj
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 4.211065e-02
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  3.711288e-14
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 8.966719e-13
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 1.649137e-11
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG 1.309158e-12
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 6.736106e-13
##                                                                                                                                                                                                                                            Kingdom
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Bacteria
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Bacteria
##                                                                                                                                                                                                                                                   Phylum
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT    Fusobacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT     Firmicutes
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT  Bacteroidetes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG     Firmicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG     Firmicutes
##                                                                                                                                                                                                                                                         Class
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT        Fusobacteriia
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT          Clostridia
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT         Bacteroidia
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG          Clostridia
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG          Clostridia
##                                                                                                                                                                                                                                                       Order
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriales
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT    Fusobacteriales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT     Clostridiales
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT     Bacteroidales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG     Clostridiales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG     Clostridiales
##                                                                                                                                                                                                                                                       Family
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriaceae
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT    Fusobacteriaceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT    Ruminococcaceae
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT     Prevotellaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG    Lachnospiraceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG    Ruminococcaceae
##                                                                                                                                                                                                                                                   Genus
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG          <NA>
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  Fusobacterium
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT  Ruminococcus
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT    Prevotella
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG          <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Oscillibacter
##                                                                                                                                                                                                                                           Species
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG    <NA>
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT     <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT    <NA>
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT    <NA>
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG    <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG    <NA>
write.table(sigtab_dds.dds.NoAbx.Narrow.d7, file="./results/deseq_d7_NoAbx.Narrow.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.NoAbx.Narrow.d7, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                            description
##                                                                                            <character>
## baseMean                                                     mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj                                                                              BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Narrow.d7 = data.table(as(results(dds.NoAbx.Narrow.d7, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Narrow.d7, "rn", "OTU")
taxdt.dds.d7.com = data.table(data.frame(as(tax_table(ps1.CvN.7), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d7.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d7.com, "OTU")
setkeyv(resdt.dds.NoAbx.Narrow.d7, "OTU")
resdt.dds.NoAbx.Narrow.d7 <- taxdt.dds.d7.com[resdt.dds.NoAbx.Narrow.d7]
resdt.dds.NoAbx.Narrow.d7
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000       0.000000
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.1590145      -1.974641
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000       0.000000
## 1588:   Prevotellaceae    Prevotella     bivia 0.4032097      -2.876681
##          lfcSE       stat    pvalue     padj
##    1:       NA         NA        NA       NA
##    2: 0.000000  0.0000000 1.0000000       NA
##    3:       NA         NA        NA       NA
##    4:       NA         NA        NA       NA
##    5: 2.978829 -0.6628915 0.5074001       NA
##   ---                                       
## 1584:       NA         NA        NA       NA
## 1585:       NA         NA        NA       NA
## 1586:       NA         NA        NA       NA
## 1587: 0.000000  0.0000000 1.0000000       NA
## 1588: 2.965841 -0.9699377 0.3320776 0.741791
resdt.dds.NoAbx.Narrow.d7[, Significant := padj < alpha]
resdt.dds.NoAbx.Narrow.d7[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 342: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 343: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCGGTTTCGTAAGTCGTGTGTGAAAGGCGGGGGCTCAACCCCCGGACTGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 344: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 345: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 346: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria      Firmicutes       Clostridia      Clostridiales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 342: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 343: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 344: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 345: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 346: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
##                   Family            Genus     Species     baseMean
##   1:     Ruminococcaceae Faecalibacterium prausnitzii 1814.6187149
##   2:     Ruminococcaceae Faecalibacterium prausnitzii 1290.2535684
##   3:     Ruminococcaceae Faecalibacterium prausnitzii 1032.7416539
##   4:     Ruminococcaceae Faecalibacterium prausnitzii   25.0903035
##   5:     Ruminococcaceae Faecalibacterium prausnitzii  182.2151655
##  ---                                                              
## 342:       Rikenellaceae        Alistipes  putredinis   68.1645873
## 343: Verrucomicrobiaceae      Akkermansia        <NA>    0.6421931
## 344: Verrucomicrobiaceae      Akkermansia muciniphila 1374.1188362
## 345:      Bacteroidaceae      Bacteroides        <NA>    2.2815044
## 346:      Prevotellaceae       Prevotella       bivia    0.4032097
##      log2FoldChange    lfcSE         stat     pvalue      padj Significant
##   1:    -1.35245320 1.732849 -0.780479732 0.43510855 0.7675539       FALSE
##   2:    -0.84392684 1.637387 -0.515410757 0.60626602 0.8291227       FALSE
##   3:    -3.88936387 1.711193 -2.272896025 0.02303245 0.4687780       FALSE
##   4:     2.09170546 2.892648  0.723110986 0.46961170 0.7782087       FALSE
##   5:    -2.09010636 1.895218 -1.102831743 0.27010024 0.7417910       FALSE
##  ---                                                                      
## 342:    -0.95403735 1.995853 -0.478009710 0.63264329 0.8479897       FALSE
## 343:    -0.02107634 2.967394 -0.007102642 0.99433296 0.9943330       FALSE
## 344:     3.11350252 1.943173  1.602277723 0.10909421 0.7417910       FALSE
## 345:    -5.10955229 2.944002 -1.735580232 0.08263808 0.7417910       FALSE
## 346:    -2.87668094 2.965841 -0.969937680 0.33207756 0.7417910       FALSE
resdt.dds.NoAbx.Narrow.d7
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000       0.000000
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.1590145      -1.974641
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000       0.000000
## 1588:   Prevotellaceae    Prevotella     bivia 0.4032097      -2.876681
##          lfcSE       stat    pvalue     padj Significant
##    1:       NA         NA        NA       NA          NA
##    2: 0.000000  0.0000000 1.0000000       NA          NA
##    3:       NA         NA        NA       NA          NA
##    4:       NA         NA        NA       NA          NA
##    5: 2.978829 -0.6628915 0.5074001       NA          NA
##   ---                                                   
## 1584:       NA         NA        NA       NA          NA
## 1585:       NA         NA        NA       NA          NA
## 1586:       NA         NA        NA       NA          NA
## 1587: 0.000000  0.0000000 1.0000000       NA          NA
## 1588: 2.965841 -0.9699377 0.3320776 0.741791       FALSE
volcano.NoAbx.Narrow.d7 = ggplot(
  data = resdt.dds.NoAbx.Narrow.d7[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.NoAbx.Narrow.d7[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 7 No Antibiotics vs Narrow Spectrum Antibiotics") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Narrow.d7

summary(res.dds.NoAbx.Narrow.d7)
## 
## out of 515 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 6, 1.2%
## LFC < 0 (down)     : 7, 1.4%
## outliers [1]       : 0, 0%
## low counts [2]     : 569, 110%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.NoAbx.Narrow.d7, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                            description
##                                                                                            <character>
## baseMean                                                     mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj                                                                              BH adjusted p-values
ggplotly(volcano.NoAbx.Narrow.d7)

#Control vs. Broad Spectrum Antibiotics

Control vs. Broad Spectrum Antibiotics Day -9

# Differential Abundance Testing
sample_data(ps1.CvB_9)$randomization_arm
##  [1] No antibiotics             No antibiotics            
##  [3] No antibiotics             No antibiotics            
##  [5] No antibiotics             Broad spectrum antibiotics
##  [7] No antibiotics             Broad spectrum antibiotics
##  [9] Broad spectrum antibiotics Broad spectrum antibiotics
## [11] No antibiotics             No antibiotics            
## [13] Broad spectrum antibiotics Broad spectrum antibiotics
## [15] No antibiotics             Broad spectrum antibiotics
## Levels: No antibiotics Broad spectrum antibiotics
sample_data(ps1.CvB_9)$day
##  [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9
ds.NoAbx.Broad.d_9 <- phyloseq_to_deseq2(ps1.CvB_9, ~randomization_arm)

geoMeans.ds.NoAbx.Broad.d_9 <- apply(counts(ds.NoAbx.Broad.d_9), 1, gm_mean)
ds.NoAbx.Broad.d_9 <- estimateSizeFactors(ds.NoAbx.Broad.d_9, geoMeans = geoMeans.ds.NoAbx.Broad.d_9)
dds.NoAbx.Broad.d_9 <- DESeq(ds.NoAbx.Broad.d_9, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.NoAbx.Broad.d_9 = results(dds.NoAbx.Broad.d_9, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Broad.d_9 = res.dds.NoAbx.Broad.d_9[which(res.dds.NoAbx.Broad.d_9$padj < alpha), ]
sigtab_dds.dds.NoAbx.Broad.d_9 = cbind(as(sigtab_dds.dds.NoAbx.Broad.d_9, "data.frame"), as(tax_table(ps1.CvB_9)[rownames(sigtab_dds.dds.NoAbx.Broad.d_9), ], "matrix"))
summary(res.dds.NoAbx.Broad.d_9)
## 
## out of 439 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 7, 1.6%
## LFC < 0 (down)     : 2, 0.46%
## outliers [1]       : 0, 0%
## low counts [2]     : 237, 54%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.NoAbx.Broad.d_9)
##                                                                                                                                                                                                                                            baseMean
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT  48.46057
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT   36.84827
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 107.08180
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG  39.38256
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT  10.43596
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG   29.17452
##                                                                                                                                                                                                                                           log2FoldChange
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT     -24.301966
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT        8.825884
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      25.355572
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG       8.921779
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT     -21.987913
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG       23.539751
##                                                                                                                                                                                                                                              lfcSE
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT 3.009187
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  2.606634
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.986930
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 2.635124
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 3.011053
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG  2.987729
##                                                                                                                                                                                                                                                stat
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT -8.075925
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT   3.385931
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  8.488841
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG  3.385715
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT -7.302400
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG   7.878811
##                                                                                                                                                                                                                                                 pvalue
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT 6.696682e-16
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  7.093713e-04
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.087089e-17
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 7.099314e-04
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 2.826789e-13
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG  3.305114e-15
##                                                                                                                                                                                                                                                   padj
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT 1.469922e-13
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  3.462888e-02
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 9.162320e-15
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 3.462888e-02
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 1.836675e-11
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG  4.836484e-13
##                                                                                                                                                                                                                                            Kingdom
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Bacteria
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteria
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG  Bacteria
##                                                                                                                                                                                                                                                  Phylum
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteroidetes
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT     Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Firmicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG    Firmicutes
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidetes
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG     Firmicutes
##                                                                                                                                                                                                                                                      Class
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT      Bacteroidia
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT     Negativicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT       Clostridia
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG       Clostridia
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT      Bacteroidia
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG  Erysipelotrichia
##                                                                                                                                                                                                                                                        Order
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT      Bacteroidales
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT     Selenomonadales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      Clostridiales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG      Clostridiales
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT      Bacteroidales
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG  Erysipelotrichales
##                                                                                                                                                                                                                                                        Family
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT      Bacteroidaceae
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT   Acidaminococcaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Ruminococcaceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG     Ruminococcaceae
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT      Bacteroidaceae
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG  Erysipelotrichaceae
##                                                                                                                                                                                                                                                           Genus
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT           Bacteroides
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  Phascolarctobacterium
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      Faecalibacterium
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG                  <NA>
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT           Bacteroides
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG           Holdemanella
##                                                                                                                                                                                                                                                 Species
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT        ovatus
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  succinatutens
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT          <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG          <NA>
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT          <NA>
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG       biformis
write.table(sigtab_dds.dds.NoAbx.Broad.d_9, file="./results/deseq_d_9_NoAbx.Broad.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.NoAbx.Broad.d_9, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                           description
##                                                                                           <character>
## baseMean                                                    mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj                                                                             BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Broad.d_9 = data.table(as(results(dds.NoAbx.Broad.d_9, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Broad.d_9, "rn", "OTU")
taxdt.dds.d_9.com = data.table(data.frame(as(tax_table(ps1.CvB_9), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d_9.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d_9.com, "OTU")
setkeyv(resdt.dds.NoAbx.Broad.d_9, "OTU")
resdt.dds.NoAbx.Broad.d_9 <- taxdt.dds.d_9.com[resdt.dds.NoAbx.Broad.d_9]
resdt.dds.NoAbx.Broad.d_9
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.5258314      -2.394897
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.2426914      -1.525241
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000       0.000000
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.0000000             NA
##          lfcSE       stat    pvalue      padj
##    1: 3.050920 -0.7849754 0.4324680 0.9326554
##    2:       NA         NA        NA        NA
##    3:       NA         NA        NA        NA
##    4: 3.070373 -0.4967609 0.6193577 0.9326554
##    5:       NA         NA        NA        NA
##   ---                                        
## 1584: 0.000000  0.0000000 1.0000000        NA
## 1585:       NA         NA        NA        NA
## 1586:       NA         NA        NA        NA
## 1587:       NA         NA        NA        NA
## 1588:       NA         NA        NA        NA
resdt.dds.NoAbx.Broad.d_9[, Significant := padj < alpha]
resdt.dds.NoAbx.Broad.d_9[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 435: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATACCGGTGCTTAACACCGGAACTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 436: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 437: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 438: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 439: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 435: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 436: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 437: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 438: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 439: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
##                   Family            Genus     Species     baseMean
##   1:     Ruminococcaceae             <NA>        <NA>    0.5258314
##   2:       Rikenellaceae             <NA>        <NA>    0.2426914
##   3:     Ruminococcaceae Faecalibacterium        <NA>  107.0817997
##   4:     Ruminococcaceae Faecalibacterium prausnitzii 1485.4282916
##   5:     Ruminococcaceae Faecalibacterium prausnitzii 1749.3431145
##  ---                                                              
## 435:       Rikenellaceae        Alistipes        <NA>    4.1518365
## 436:       Rikenellaceae        Alistipes        <NA>   33.8081991
## 437:       Rikenellaceae        Alistipes  putredinis  281.3899873
## 438: Verrucomicrobiaceae      Akkermansia muciniphila  199.8373307
## 439:      Bacteroidaceae      Bacteroides        <NA>   10.4359558
##      log2FoldChange    lfcSE        stat       pvalue         padj
##   1:    -2.39489729 3.050920 -0.78497541 4.324680e-01 9.326554e-01
##   2:    -1.52524141 3.070373 -0.49676091 6.193577e-01 9.326554e-01
##   3:    25.35557225 2.986930  8.48884053 2.087089e-17 9.162320e-15
##   4:     0.46395927 1.095888  0.42336376 6.720299e-01 9.326554e-01
##   5:    -0.09913691 1.013482 -0.09781812 9.220767e-01 9.707235e-01
##  ---                                                              
## 435:     5.63512784 2.992748  1.88292770 5.971017e-02 9.326554e-01
## 436:     2.26652658 2.913005  0.77807157 4.365268e-01 9.326554e-01
## 437:     0.30039331 1.357168  0.22133832 8.248290e-01 9.412854e-01
## 438:    -1.60459534 2.347218 -0.68361577 4.942178e-01 9.326554e-01
## 439:   -21.98791258 3.011053 -7.30240023 2.826789e-13 1.836675e-11
##      Significant
##   1:       FALSE
##   2:       FALSE
##   3:        TRUE
##   4:       FALSE
##   5:       FALSE
##  ---            
## 435:       FALSE
## 436:       FALSE
## 437:       FALSE
## 438:       FALSE
## 439:        TRUE
resdt.dds.NoAbx.Broad.d_9
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.5258314      -2.394897
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.2426914      -1.525241
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000       0.000000
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.0000000             NA
##          lfcSE       stat    pvalue      padj Significant
##    1: 3.050920 -0.7849754 0.4324680 0.9326554       FALSE
##    2:       NA         NA        NA        NA          NA
##    3:       NA         NA        NA        NA          NA
##    4: 3.070373 -0.4967609 0.6193577 0.9326554       FALSE
##    5:       NA         NA        NA        NA          NA
##   ---                                                    
## 1584: 0.000000  0.0000000 1.0000000        NA          NA
## 1585:       NA         NA        NA        NA          NA
## 1586:       NA         NA        NA        NA          NA
## 1587:       NA         NA        NA        NA          NA
## 1588:       NA         NA        NA        NA          NA
volcano.NoAbx.Broad.d_9 = ggplot(
  data = resdt.dds.NoAbx.Broad.d_9[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.NoAbx.Broad.d_9[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay -9 No Antibiotics vs Broad Spectrum Antibiotics") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Broad.d_9

summary(res.dds.NoAbx.Broad.d_9)
## 
## out of 439 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 7, 1.6%
## LFC < 0 (down)     : 2, 0.46%
## outliers [1]       : 0, 0%
## low counts [2]     : 237, 54%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.NoAbx.Broad.d_9, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                           description
##                                                                                           <character>
## baseMean                                                    mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj                                                                             BH adjusted p-values
ggplotly(volcano.NoAbx.Broad.d_9)

##Control vs. Broad Spectrum Antibiotics Day 0

# Differential Abundance Testing
sample_data(ps1.CvB.0)$randomization_arm
##  [1] Broad spectrum antibiotics No antibiotics            
##  [3] No antibiotics             Broad spectrum antibiotics
##  [5] Broad spectrum antibiotics No antibiotics            
##  [7] No antibiotics             No antibiotics            
##  [9] Broad spectrum antibiotics No antibiotics            
## [11] No antibiotics             No antibiotics            
## [13] Broad spectrum antibiotics No antibiotics            
## [15] Broad spectrum antibiotics Broad spectrum antibiotics
## [17] No antibiotics             Broad spectrum antibiotics
## [19] Broad spectrum antibiotics No antibiotics            
## [21] Broad spectrum antibiotics
## Levels: No antibiotics Broad spectrum antibiotics
sample_data(ps1.CvB.0)$day
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ds.NoAbx.Broad.d0 <- phyloseq_to_deseq2(ps1.CvB.0, ~randomization_arm)

geoMeans.ds.NoAbx.Broad.d0 <- apply(counts(ds.NoAbx.Broad.d0), 1, gm_mean)
ds.NoAbx.Broad.d0 <- estimateSizeFactors(ds.NoAbx.Broad.d0, geoMeans = geoMeans.ds.NoAbx.Broad.d0)
dds.NoAbx.Broad.d0 <- DESeq(ds.NoAbx.Broad.d0, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.NoAbx.Broad.d0 = results(dds.NoAbx.Broad.d0, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Broad.d0 = res.dds.NoAbx.Broad.d0[which(res.dds.NoAbx.Broad.d0$padj < alpha), ]
sigtab_dds.dds.NoAbx.Broad.d0 = cbind(as(sigtab_dds.dds.NoAbx.Broad.d0, "data.frame"), as(tax_table(ps1.CvB_9)[rownames(sigtab_dds.dds.NoAbx.Broad.d0), ], "matrix"))
summary(res.dds.NoAbx.Broad.d0)
## 
## out of 382 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 9, 2.4%
## LFC < 0 (down)     : 77, 20%
## outliers [1]       : 0, 0%
## low counts [2]     : 455, 119%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.NoAbx.Broad.d0)
##                                                                                                                                                                                                                                           baseMean
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 301.5808
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 451.8549
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 257.5498
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 165.9611
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 150.5424
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 168.1260
##                                                                                                                                                                                                                                           log2FoldChange
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      -6.474862
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      -4.608870
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      -5.193611
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      -6.902948
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT      -6.606505
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG      -3.872766
##                                                                                                                                                                                                                                              lfcSE
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.648019
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.303228
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.404298
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.600530
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.916255
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 1.681402
##                                                                                                                                                                                                                                                stat
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -3.928876
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -3.536503
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -3.698367
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -4.312913
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -2.265407
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG -2.303296
##                                                                                                                                                                                                                                                 pvalue
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 8.534366e-05
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 4.054615e-04
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.169911e-04
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.611174e-05
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.348773e-02
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 2.126220e-02
##                                                                                                                                                                                                                                                   padj
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 0.0004830121
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 0.0018089000
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 0.0010270911
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 0.0001271037
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 0.0497799681
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 0.0471755171
##                                                                                                                                                                                                                                            Kingdom
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Bacteria
##                                                                                                                                                                                                                                                  Phylum
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Firmicutes
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidetes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG    Firmicutes
##                                                                                                                                                                                                                                                 Class
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  Clostridia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  Clostridia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  Clostridia
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidia
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG  Clostridia
##                                                                                                                                                                                                                                                   Order
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Clostridiales
##                                                                                                                                                                                                                                                    Family
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT  Bacteroidaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT  Prevotellaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Lachnospiraceae
##                                                                                                                                                                                                                                                      Genus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      Bacteroides
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT       Prevotella
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG        Roseburia
##                                                                                                                                                                                                                                               Species
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT        <NA>
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT        <NA>
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG        <NA>
write.table(sigtab_dds.dds.NoAbx.Broad.d0, file="./results/deseq_d0_NoAbx.Broad.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.NoAbx.Broad.d0, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                           description
##                                                                                           <character>
## baseMean                                                    mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj                                                                             BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Broad.d0 = data.table(as(results(dds.NoAbx.Broad.d0, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Broad.d0, "rn", "OTU")
taxdt.dds.d0.com = data.table(data.frame(as(tax_table(ps1.CvB.0), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d0.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d0.com, "OTU")
setkeyv(resdt.dds.NoAbx.Broad.d0, "OTU")
resdt.dds.NoAbx.Broad.d0 <- taxdt.dds.d0.com[resdt.dds.NoAbx.Broad.d0]
resdt.dds.NoAbx.Broad.d0
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA>        0             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum        0             NA
##    3: Fusobacteriaceae Fusobacterium      <NA>        0             NA
##    4:    Rikenellaceae          <NA>      <NA>        0             NA
##    5:   Eubacteriaceae   Eubacterium      <NA>        0             NA
##   ---                                                                 
## 1584:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris        0             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1587:   Prevotellaceae    Prevotella      <NA>        0              0
## 1588:   Prevotellaceae    Prevotella     bivia        0             NA
##       lfcSE stat pvalue padj
##    1:    NA   NA     NA   NA
##    2:    NA   NA     NA   NA
##    3:    NA   NA     NA   NA
##    4:    NA   NA     NA   NA
##    5:    NA   NA     NA   NA
##   ---                       
## 1584:    NA   NA     NA   NA
## 1585:    NA   NA     NA   NA
## 1586:    NA   NA     NA   NA
## 1587:     0    0      1   NA
## 1588:    NA   NA     NA   NA
resdt.dds.NoAbx.Broad.d0[, Significant := padj < alpha]
resdt.dds.NoAbx.Broad.d0[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 138:  GCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 139:  GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 140: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 141: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 142: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria      Firmicutes       Clostridia      Clostridiales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 138: Bacteria      Firmicutes       Clostridia      Clostridiales
## 139: Bacteria      Firmicutes       Clostridia      Clostridiales
## 140: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 141: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 142: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
##                     Family            Genus     Species   baseMean
##   1:       Ruminococcaceae Faecalibacterium prausnitzii 301.580788
##   2:       Ruminococcaceae Faecalibacterium prausnitzii 451.854908
##   3:       Ruminococcaceae Faecalibacterium prausnitzii 165.961121
##   4:       Ruminococcaceae Faecalibacterium prausnitzii  85.555734
##   5:       Ruminococcaceae Faecalibacterium prausnitzii  57.703750
##  ---                                                              
## 138: Peptostreptococcaceae       Romboutsia        <NA>   9.709707
## 139: Peptostreptococcaceae  Intestinibacter  bartlettii   3.445454
## 140:         Rikenellaceae        Alistipes        <NA>   3.460808
## 141:         Rikenellaceae        Alistipes  putredinis  42.467602
## 142:   Verrucomicrobiaceae      Akkermansia muciniphila  40.794006
##      log2FoldChange    lfcSE       stat       pvalue         padj
##   1:      -6.474862 1.648019 -3.9288765 8.534366e-05 4.830121e-04
##   2:      -4.608870 1.303228 -3.5365033 4.054615e-04 1.808900e-03
##   3:      -6.902948 1.600530 -4.3129132 1.611174e-05 1.271037e-04
##   4:     -25.076665 2.959964 -8.4719506 2.413175e-17 8.566771e-16
##   5:      -8.375351 1.820343 -4.6009750 4.205178e-06 3.732096e-05
##  ---                                                             
## 138:      -2.854650 2.072512 -1.3773866 1.683927e-01 1.882817e-01
## 139:      -2.117982 2.931472 -0.7224979 4.699884e-01 4.733216e-01
## 140:      -4.309155 2.823670 -1.5260828 1.269892e-01 1.505607e-01
## 141:      -7.932753 1.879065 -4.2216482 2.425224e-05 1.812536e-04
## 142:      -2.315473 2.036296 -1.1371007 2.554962e-01 2.648209e-01
##      Significant
##   1:        TRUE
##   2:        TRUE
##   3:        TRUE
##   4:        TRUE
##   5:        TRUE
##  ---            
## 138:       FALSE
## 139:       FALSE
## 140:       FALSE
## 141:        TRUE
## 142:       FALSE
resdt.dds.NoAbx.Broad.d0
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA>        0             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum        0             NA
##    3: Fusobacteriaceae Fusobacterium      <NA>        0             NA
##    4:    Rikenellaceae          <NA>      <NA>        0             NA
##    5:   Eubacteriaceae   Eubacterium      <NA>        0             NA
##   ---                                                                 
## 1584:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris        0             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1587:   Prevotellaceae    Prevotella      <NA>        0              0
## 1588:   Prevotellaceae    Prevotella     bivia        0             NA
##       lfcSE stat pvalue padj Significant
##    1:    NA   NA     NA   NA          NA
##    2:    NA   NA     NA   NA          NA
##    3:    NA   NA     NA   NA          NA
##    4:    NA   NA     NA   NA          NA
##    5:    NA   NA     NA   NA          NA
##   ---                                   
## 1584:    NA   NA     NA   NA          NA
## 1585:    NA   NA     NA   NA          NA
## 1586:    NA   NA     NA   NA          NA
## 1587:     0    0      1   NA          NA
## 1588:    NA   NA     NA   NA          NA
volcano.NoAbx.Broad.d0 = ggplot(
  data = resdt.dds.NoAbx.Broad.d0[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.NoAbx.Broad.d0[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 0 No Antibiotics vs Broad Spectrum Antibiotics") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Broad.d0

summary(res.dds.NoAbx.Broad.d0)
## 
## out of 382 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 9, 2.4%
## LFC < 0 (down)     : 77, 20%
## outliers [1]       : 0, 0%
## low counts [2]     : 455, 119%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.NoAbx.Broad.d0, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                           description
##                                                                                           <character>
## baseMean                                                    mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj                                                                             BH adjusted p-values
ggplotly(volcano.NoAbx.Broad.d0)

##Control vs. Broad Spectrum Antibiotics Day 7

# Differential Abundance Testing
sample_data(ps1.CvB.7)$randomization_arm
##  [1] Broad spectrum antibiotics Broad spectrum antibiotics
##  [3] No antibiotics             No antibiotics            
##  [5] Broad spectrum antibiotics No antibiotics            
##  [7] No antibiotics             No antibiotics            
##  [9] Broad spectrum antibiotics Broad spectrum antibiotics
## [11] No antibiotics             No antibiotics            
## [13] No antibiotics             Broad spectrum antibiotics
## [15] Broad spectrum antibiotics No antibiotics            
## [17] Broad spectrum antibiotics Broad spectrum antibiotics
## [19] Broad spectrum antibiotics No antibiotics            
## [21] No antibiotics             Broad spectrum antibiotics
## [23] Broad spectrum antibiotics No antibiotics            
## [25] Broad spectrum antibiotics No antibiotics            
## [27] No antibiotics            
## Levels: No antibiotics Broad spectrum antibiotics
sample_data(ps1.CvB.7)$day
##  [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
ds.NoAbx.Broad.d7 <- phyloseq_to_deseq2(ps1.CvB.7, ~randomization_arm)

geoMeans.ds.NoAbx.Broad.d7 <- apply(counts(ds.NoAbx.Broad.d7), 1, gm_mean)
ds.NoAbx.Broad.d7 <- estimateSizeFactors(ds.NoAbx.Broad.d7, geoMeans = geoMeans.ds.NoAbx.Broad.d7)

dds.NoAbx.Broad.d7 <- DESeq(ds.NoAbx.Broad.d7, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.NoAbx.Broad.d7 = results(dds.NoAbx.Broad.d7, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Broad.d7 = res.dds.NoAbx.Broad.d7[which(res.dds.NoAbx.Broad.d7$padj < alpha), ]
sigtab_dds.dds.NoAbx.Broad.d7 = cbind(as(sigtab_dds.dds.NoAbx.Broad.d7, "data.frame"), as(tax_table(ps1.CvB_9)[rownames(sigtab_dds.dds.NoAbx.Broad.d7), ], "matrix"))
summary(res.dds.NoAbx.Broad.d7)
## 
## out of 480 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 8, 1.7%
## LFC < 0 (down)     : 36, 7.5%
## outliers [1]       : 0, 0%
## low counts [2]     : 615, 128%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.NoAbx.Broad.d7)
##                                                                                                                                                                                                                                            baseMean
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT  78.35087
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT  33.27089
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT   14.17571
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT  12.43114
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 119.84760
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG  26.11912
##                                                                                                                                                                                                                                           log2FoldChange
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT      -6.319005
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT     -25.007883
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT       22.899554
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT     -23.677355
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     -10.440964
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG      -7.057749
##                                                                                                                                                                                                                                              lfcSE
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.061875
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.942847
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  2.939721
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.943814
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.051790
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG 2.454841
##                                                                                                                                                                                                                                                stat
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -3.064689
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -8.497853
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT   7.789702
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -8.043087
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -5.088710
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG -2.875033
##                                                                                                                                                                                                                                                 pvalue
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.178966e-03
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 1.931298e-17
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  6.716737e-15
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 8.760291e-16
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 3.605066e-07
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG 4.039856e-03
##                                                                                                                                                                                                                                                   padj
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 1.357663e-02
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 7.821758e-16
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  1.088111e-13
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.027382e-14
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 3.893471e-06
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG 2.423914e-02
##                                                                                                                                                                                                                                            Kingdom
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG Bacteria
##                                                                                                                                                                                                                                                  Phylum
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT   Fusobacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidetes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Firmicutes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG    Firmicutes
##                                                                                                                                                                                                                                                   Class
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidia
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidia
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  Fusobacteriia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT   Bacteroidia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Clostridia
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG    Clostridia
##                                                                                                                                                                                                                                                     Order
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidales
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  Fusobacteriales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT   Bacteroidales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT   Clostridiales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG   Clostridiales
##                                                                                                                                                                                                                                                     Family
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Prevotellaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Prevotellaceae
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT  Fusobacteriaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT   Bacteroidaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  Ruminococcaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG  Ruminococcaceae
##                                                                                                                                                                                                                                                      Genus
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT       Prevotella
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT       Prevotella
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT     Fusobacterium
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      Bacteroides
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG   Clostridium_IV
##                                                                                                                                                                                                                                               Species
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT       copri
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT        <NA>
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT         <NA>
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT       dorei
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG        <NA>
write.table(sigtab_dds.dds.NoAbx.Broad.d7, file="./results/deseq_d7_NoAbx.Broad.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.NoAbx.Broad.d7, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                           description
##                                                                                           <character>
## baseMean                                                    mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj                                                                             BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Broad.d7 = data.table(as(results(dds.NoAbx.Broad.d7, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Broad.d7, "rn", "OTU")
taxdt.dds.d7.com = data.table(data.frame(as(tax_table(ps1.CvB.7), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d7.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d7.com, "OTU")
setkeyv(resdt.dds.NoAbx.Broad.d7, "OTU")
resdt.dds.NoAbx.Broad.d7 <- taxdt.dds.d7.com[resdt.dds.NoAbx.Broad.d7]
resdt.dds.NoAbx.Broad.d7
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.1383900      -1.388132
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.3537685      -2.308552
##          lfcSE       stat    pvalue padj
##    1:       NA         NA        NA   NA
##    2:       NA         NA        NA   NA
##    3:       NA         NA        NA   NA
##    4:       NA         NA        NA   NA
##    5: 2.986302 -0.4648330 0.6420510   NA
##   ---                                   
## 1584:       NA         NA        NA   NA
## 1585:       NA         NA        NA   NA
## 1586:       NA         NA        NA   NA
## 1587:       NA         NA        NA   NA
## 1588: 2.973104 -0.7764788 0.4374663   NA
resdt.dds.NoAbx.Broad.d7[, Significant := padj < alpha]
resdt.dds.NoAbx.Broad.d7[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 158:  GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 159:  GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 160: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 161: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 162: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria      Firmicutes       Clostridia      Clostridiales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 158: Bacteria      Firmicutes       Clostridia      Clostridiales
## 159: Bacteria      Firmicutes       Clostridia      Clostridiales
## 160: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 161: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 162: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
##                     Family            Genus     Species    baseMean
##   1:       Ruminococcaceae Faecalibacterium prausnitzii 1153.752813
##   2:       Ruminococcaceae Faecalibacterium prausnitzii  766.717491
##   3:       Ruminococcaceae Faecalibacterium prausnitzii  939.220027
##   4:       Ruminococcaceae Faecalibacterium prausnitzii    8.549888
##   5:       Ruminococcaceae Faecalibacterium prausnitzii  119.847602
##  ---                                                               
## 158: Peptostreptococcaceae  Intestinibacter  bartlettii   37.167487
## 159: Peptostreptococcaceae Terrisporobacter        <NA>    2.827404
## 160:         Rikenellaceae        Alistipes        <NA>    4.356718
## 161:         Rikenellaceae        Alistipes  putredinis   36.369725
## 162:   Verrucomicrobiaceae      Akkermansia muciniphila   14.896185
##      log2FoldChange    lfcSE       stat       pvalue         padj
##   1:     -3.3601695 1.848755 -1.8175308 6.913588e-02 1.754690e-01
##   2:     -2.6520420 1.700302 -1.5597474 1.188196e-01 2.499841e-01
##   3:     -3.2863627 1.978889 -1.6607108 9.677154e-02 2.118512e-01
##   4:      0.5435519 2.896667  0.1876473 8.511531e-01 9.254148e-01
##   5:    -10.4409639 2.051790 -5.0887104 3.605066e-07 3.893471e-06
##  ---                                                             
## 158:     -0.6025818 1.098780 -0.5484098 5.834106e-01 7.326551e-01
## 159:     -1.9673140 2.813765 -0.6991750 4.844427e-01 6.715397e-01
## 160:     -2.6660195 2.625130 -1.0155764 3.098312e-01 4.920848e-01
## 161:     -8.7213663 2.032772 -4.2903802 1.783675e-05 1.699737e-04
## 162:     -5.5548758 2.259363 -2.4586032 1.394787e-02 6.679572e-02
##      Significant
##   1:       FALSE
##   2:       FALSE
##   3:       FALSE
##   4:       FALSE
##   5:        TRUE
##  ---            
## 158:       FALSE
## 159:       FALSE
## 160:       FALSE
## 161:        TRUE
## 162:       FALSE
resdt.dds.NoAbx.Broad.d7
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.0000000             NA
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.1383900      -1.388132
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.3537685      -2.308552
##          lfcSE       stat    pvalue padj Significant
##    1:       NA         NA        NA   NA          NA
##    2:       NA         NA        NA   NA          NA
##    3:       NA         NA        NA   NA          NA
##    4:       NA         NA        NA   NA          NA
##    5: 2.986302 -0.4648330 0.6420510   NA          NA
##   ---                                               
## 1584:       NA         NA        NA   NA          NA
## 1585:       NA         NA        NA   NA          NA
## 1586:       NA         NA        NA   NA          NA
## 1587:       NA         NA        NA   NA          NA
## 1588: 2.973104 -0.7764788 0.4374663   NA          NA
volcano.NoAbx.Broad.d7 = ggplot(
  data = resdt.dds.NoAbx.Broad.d7[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.NoAbx.Broad.d7[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 7 No Antibiotics vs Broad Spectrum Antibiotics") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Broad.d7

summary(res.dds.NoAbx.Broad.d7)
## 
## out of 480 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 8, 1.7%
## LFC < 0 (down)     : 36, 7.5%
## outliers [1]       : 0, 0%
## low counts [2]     : 615, 128%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.NoAbx.Broad.d7, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                           description
##                                                                                           <character>
## baseMean                                                    mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj                                                                             BH adjusted p-values
ggplotly(volcano.NoAbx.Broad.d7)

##Narrow vs. Broad Spectrum Antibiotics Day -9

# Differential Abundance Testing
sample_data(ps1.NvB_9)$randomization_arm
##  [1] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [3] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [5] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [7] Broad spectrum antibiotics  Broad spectrum antibiotics 
##  [9] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [13] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [15] Broad spectrum antibiotics 
## Levels: Narrow spectrum antibiotics Broad spectrum antibiotics
sample_data(ps1.NvB_9)$day
##  [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9
ds.Narrow.Broad.d_9 <- phyloseq_to_deseq2(ps1.NvB_9, ~randomization_arm)

geoMeans.ds.Narrow.Broad.d_9 <- apply(counts(ds.Narrow.Broad.d_9), 1, gm_mean)
ds.Narrow.Broad.d_9 <- estimateSizeFactors(ds.Narrow.Broad.d_9, geoMeans = geoMeans.ds.Narrow.Broad.d_9)
dds.Narrow.Broad.d_9 <- DESeq(ds.Narrow.Broad.d_9, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.Narrow.Broad.d_9 = results(dds.Narrow.Broad.d_9, cooksCutoff = FALSE)
sigtab_dds.dds.Narrow.Broad.d_9 = res.dds.Narrow.Broad.d_9[which(res.dds.Narrow.Broad.d_9$padj < alpha), ]
sigtab_dds.dds.Narrow.Broad.d_9 = cbind(as(sigtab_dds.dds.Narrow.Broad.d_9, "data.frame"), as(tax_table(ps1.NvB_9)[rownames(sigtab_dds.dds.Narrow.Broad.d_9), ], "matrix"))
summary(res.dds.Narrow.Broad.d_9)
## 
## out of 433 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 7, 1.6%
## LFC < 0 (down)     : 4, 0.92%
## outliers [1]       : 0, 0%
## low counts [2]     : 244, 56%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.Narrow.Broad.d_9)
##                                                                                                                                                                                                                                            baseMean
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG   9.286714
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 28.824996
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 14.869014
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 61.275991
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  8.975456
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 31.766339
##                                                                                                                                                                                                                                           log2FoldChange
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG       -21.23749
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT      -23.63082
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT      -22.70510
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG       24.20964
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT       21.53610
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT      -23.76015
##                                                                                                                                                                                                                                              lfcSE
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  2.995429
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.993679
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 2.994462
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 2.980967
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.984233
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 2.993603
##                                                                                                                                                                                                                                                stat
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  -7.089968
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -7.893571
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT -7.582366
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG  8.121405
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  7.216631
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT -7.936973
##                                                                                                                                                                                                                                                 pvalue
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  1.341430e-12
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.936607e-15
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 3.393089e-14
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 4.608170e-16
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 5.329151e-13
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 2.071748e-15
##                                                                                                                                                                                                                                                   padj
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  5.808391e-11
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 4.238503e-13
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 2.938415e-12
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 1.995337e-13
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.563914e-11
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 4.238503e-13
##                                                                                                                                                                                                                                            Kingdom
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteria
##                                                                                                                                                                                                                                                  Phylum
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG     Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT    Firmicutes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG    Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Firmicutes
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidetes
##                                                                                                                                                                                                                                                   Class
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  Negativicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidia
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT       Bacilli
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG    Clostridia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    Clostridia
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT   Bacteroidia
##                                                                                                                                                                                                                                                     Order
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG  Selenomonadales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Bacteroidales
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG   Clostridiales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT   Clostridiales
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT   Bacteroidales
##                                                                                                                                                                                                                                                     Family
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG   Veillonellaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT   Prevotellaceae
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG  Lachnospiraceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT  Ruminococcaceae
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT   Bacteroidaceae
##                                                                                                                                                                                                                                                      Genus
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG         Dialister
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT       Prevotella
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT    Lactobacillus
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG      Coprococcus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT      Bacteroides
##                                                                                                                                                                                                                                           Species
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG     <NA>
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT    <NA>
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT ruminis
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG    <NA>
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT    <NA>
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT    <NA>
write.table(sigtab_dds.dds.Narrow.Broad.d_9, file="./results/deseq_d_9_Narrow.Broad.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.Narrow.Broad.d_9, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                                        description
##                                                                                                        <character>
## baseMean                                                                 mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj                                                                                          BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.Narrow.Broad.d_9 = data.table(as(results(dds.Narrow.Broad.d_9, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.Narrow.Broad.d_9, "rn", "OTU")
taxdt.dds.d_9.com = data.table(data.frame(as(tax_table(ps1.NvB_9), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d_9.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d_9.com, "OTU")
setkeyv(resdt.dds.Narrow.Broad.d_9, "OTU")
resdt.dds.Narrow.Broad.d_9 <- taxdt.dds.d_9.com[resdt.dds.Narrow.Broad.d_9]
resdt.dds.Narrow.Broad.d_9
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA>        0             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum        0             NA
##    3: Fusobacteriaceae Fusobacterium      <NA>        0             NA
##    4:    Rikenellaceae          <NA>      <NA>        0             NA
##    5:   Eubacteriaceae   Eubacterium      <NA>        0             NA
##   ---                                                                 
## 1584:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris        0              0
## 1586:   Bacteroidaceae   Bacteroides      <NA>        0              0
## 1587:   Prevotellaceae    Prevotella      <NA>        0             NA
## 1588:   Prevotellaceae    Prevotella     bivia        0             NA
##       lfcSE stat pvalue padj
##    1:    NA   NA     NA   NA
##    2:    NA   NA     NA   NA
##    3:    NA   NA     NA   NA
##    4:    NA   NA     NA   NA
##    5:    NA   NA     NA   NA
##   ---                       
## 1584:    NA   NA     NA   NA
## 1585:     0    0      1   NA
## 1586:     0    0      1   NA
## 1587:    NA   NA     NA   NA
## 1588:    NA   NA     NA   NA
resdt.dds.Narrow.Broad.d_9[, Significant := padj < alpha]
resdt.dds.Narrow.Broad.d_9[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTTTTCTTGAGTGCTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGACAGTAACTGACGTTGAGGCACGAAAGCGTGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 429: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATACCGGTGCTTAACACCGGAACTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 430: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATACCGGTGCTTAACACCGGAACTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 431: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 432: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 433: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria      Firmicutes       Clostridia      Clostridiales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 429: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 430: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 431: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 432: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 433: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
##                   Family            Genus     Species     baseMean
##   1:     Ruminococcaceae Faecalibacterium        <NA>    8.9754565
##   2:     Ruminococcaceae Faecalibacterium        <NA>    0.2464832
##   3:     Ruminococcaceae Faecalibacterium prausnitzii 1960.0861755
##   4:     Ruminococcaceae Faecalibacterium        <NA>    8.6146004
##   5:     Ruminococcaceae Faecalibacterium prausnitzii 1679.8805890
##  ---                                                              
## 429:       Rikenellaceae        Alistipes        <NA>    0.2760191
## 430:       Rikenellaceae        Alistipes        <NA>    4.4005966
## 431:       Rikenellaceae        Alistipes        <NA>   48.4453684
## 432:       Rikenellaceae        Alistipes  putredinis  218.3859563
## 433: Verrucomicrobiaceae      Akkermansia muciniphila   19.4005140
##      log2FoldChange     lfcSE        stat       pvalue         padj
##   1:    21.53610404 2.9842326  7.21663053 5.329151e-13 2.563914e-11
##   2:    -1.45424762 3.0642601 -0.47458361 6.350838e-01 9.376016e-01
##   3:    -0.25298945 0.9812585 -0.25782142 7.965447e-01 9.376016e-01
##   4:    -6.39842244 2.9956388 -2.13591254 3.268654e-02 7.911656e-01
##   5:     0.01459924 0.8581001  0.01701345 9.864259e-01 9.932996e-01
##  ---                                                               
## 429:     2.06951051 3.0405947  0.68062689 4.961076e-01 9.376016e-01
## 430:     5.68626669 2.9884024  1.90277813 5.706950e-02 9.376016e-01
## 431:     0.50772192 1.8421687  0.27561098 7.828469e-01 9.376016e-01
## 432:     1.21087498 1.2038839  1.00580710 3.145084e-01 9.376016e-01
## 433:    -1.54544451 2.9013374 -0.53266625 5.942646e-01 9.376016e-01
##      Significant
##   1:        TRUE
##   2:       FALSE
##   3:       FALSE
##   4:       FALSE
##   5:       FALSE
##  ---            
## 429:       FALSE
## 430:       FALSE
## 431:       FALSE
## 432:       FALSE
## 433:       FALSE
resdt.dds.Narrow.Broad.d_9
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA>        0             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum        0             NA
##    3: Fusobacteriaceae Fusobacterium      <NA>        0             NA
##    4:    Rikenellaceae          <NA>      <NA>        0             NA
##    5:   Eubacteriaceae   Eubacterium      <NA>        0             NA
##   ---                                                                 
## 1584:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris        0              0
## 1586:   Bacteroidaceae   Bacteroides      <NA>        0              0
## 1587:   Prevotellaceae    Prevotella      <NA>        0             NA
## 1588:   Prevotellaceae    Prevotella     bivia        0             NA
##       lfcSE stat pvalue padj Significant
##    1:    NA   NA     NA   NA          NA
##    2:    NA   NA     NA   NA          NA
##    3:    NA   NA     NA   NA          NA
##    4:    NA   NA     NA   NA          NA
##    5:    NA   NA     NA   NA          NA
##   ---                                   
## 1584:    NA   NA     NA   NA          NA
## 1585:     0    0      1   NA          NA
## 1586:     0    0      1   NA          NA
## 1587:    NA   NA     NA   NA          NA
## 1588:    NA   NA     NA   NA          NA
volcano.Narrow.Broad.d_9 = ggplot(
  data = resdt.dds.Narrow.Broad.d_9[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.Narrow.Broad.d_9[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay -9 Narrow Spectrum vs Broad Spectrum") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.Narrow.Broad.d_9

summary(res.dds.Narrow.Broad.d_9)
## 
## out of 433 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 7, 1.6%
## LFC < 0 (down)     : 4, 0.92%
## outliers [1]       : 0, 0%
## low counts [2]     : 244, 56%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.Narrow.Broad.d_9, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                                        description
##                                                                                                        <character>
## baseMean                                                                 mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj                                                                                          BH adjusted p-values
ggplotly(volcano.Narrow.Broad.d_9)

##Narrow vs. Broad Spectrum Antibiotics Day 0

# Differential Abundance Testing
sample_data(ps1.NvB.0)$randomization_arm
##  [1] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [3] Broad spectrum antibiotics  Broad spectrum antibiotics 
##  [5] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
##  [9] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [13] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [15] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [17] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [19] Broad spectrum antibiotics  Broad spectrum antibiotics 
## [21] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [23] Broad spectrum antibiotics 
## Levels: Narrow spectrum antibiotics Broad spectrum antibiotics
sample_data(ps1.NvB.0)$day
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ds.Narrow.Broad.d0 <- phyloseq_to_deseq2(ps1.NvB.0, ~randomization_arm)

geoMeans.ds.Narrow.Broad.d0 <- apply(counts(ds.Narrow.Broad.d0), 1, gm_mean)
ds.Narrow.Broad.d0 <- estimateSizeFactors(ds.Narrow.Broad.d0, geoMeans = geoMeans.ds.Narrow.Broad.d0)
dds.Narrow.Broad.d0 <- DESeq(ds.Narrow.Broad.d0, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.Narrow.Broad.d0 = results(dds.Narrow.Broad.d0, cooksCutoff = FALSE)
sigtab_dds.dds.Narrow.Broad.d0 = res.dds.Narrow.Broad.d0[which(res.dds.Narrow.Broad.d0$padj < alpha), ]
sigtab_dds.dds.Narrow.Broad.d0 = cbind(as(sigtab_dds.dds.Narrow.Broad.d0, "data.frame"), as(tax_table(ps1.NvB_9)[rownames(sigtab_dds.dds.Narrow.Broad.d0), ], "matrix"))
summary(res.dds.Narrow.Broad.d0)
## 
## out of 170 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 4, 2.4%
## LFC < 0 (down)     : 15, 8.8%
## outliers [1]       : 0, 0%
## low counts [2]     : 205, 121%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.Narrow.Broad.d0)
##                                                                                                                                                                                                                                            baseMean
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT   10.1113
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  604.4930
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1956.7376
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT   349.3098
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT  268.8318
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG  155.9442
##                                                                                                                                                                                                                                           log2FoldChange
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT       7.630586
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG      -3.972833
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG      -8.544751
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT      -26.533491
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT      -8.559492
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG     -25.425020
##                                                                                                                                                                                                                                              lfcSE
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.591135
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.261105
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.418316
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  2.391792
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT 1.565761
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG 2.504133
##                                                                                                                                                                                                                                                 stat
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT   2.944881
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  -3.150278
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  -6.024575
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  -11.093563
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT  -5.466667
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG -10.153222
##                                                                                                                                                                                                                                                 pvalue
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 3.230785e-03
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.631149e-03
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.695551e-09
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  1.348095e-28
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT 4.585763e-08
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG 3.206019e-24
##                                                                                                                                                                                                                                                   padj
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.087584e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.141804e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 2.034661e-08
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  1.132400e-26
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT 4.815051e-07
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG 1.346528e-22
##                                                                                                                                                                                                                                            Kingdom
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  Bacteria
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Bacteria
##                                                                                                                                                                                                                                                   Phylum
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Firmicutes
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT      Firmicutes
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Proteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Proteobacteria
##                                                                                                                                                                                                                                                         Class
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT          Clostridia
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT        Negativicutes
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Deltaproteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG  Betaproteobacteria
##                                                                                                                                                                                                                                                        Order
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      Clostridiales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  Enterobacteriales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  Enterobacteriales
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT     Selenomonadales
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Desulfovibrionales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG    Burkholderiales
##                                                                                                                                                                                                                                                        Family
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Ruminococcaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  Enterobacteriaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  Enterobacteriaceae
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT   Acidaminococcaceae
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Desulfovibrionaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG      Sutterellaceae
##                                                                                                                                                                                                                                                           Genus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      Faecalibacterium
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG  Escherichia/Shigella
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG                  <NA>
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT  Phascolarctobacterium
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT             Bilophila
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG            Sutterella
##                                                                                                                                                                                                                                                 Species
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT   prausnitzii
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG          <NA>
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG          <NA>
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT        faecium
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT   wadsworthia
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG stercoricanis
write.table(sigtab_dds.dds.Narrow.Broad.d0, file="./results/deseq_d0_Narrow.Broad.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.Narrow.Broad.d0, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                                        description
##                                                                                                        <character>
## baseMean                                                                 mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj                                                                                          BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.Narrow.Broad.d0 = data.table(as(results(dds.Narrow.Broad.d0, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.Narrow.Broad.d0, "rn", "OTU")
taxdt.dds.d0.com = data.table(data.frame(as(tax_table(ps1.NvB.0), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d0.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d0.com, "OTU")
setkeyv(resdt.dds.Narrow.Broad.d0, "OTU")
resdt.dds.Narrow.Broad.d0 <- taxdt.dds.d0.com[resdt.dds.Narrow.Broad.d0]
resdt.dds.Narrow.Broad.d0
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.1663214    -0.06235141
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.3416447    -0.83260200
##          lfcSE        stat    pvalue padj
##    1:       NA          NA        NA   NA
##    2:       NA          NA        NA   NA
##    3: 3.021907 -0.02063313 0.9835383   NA
##    4:       NA          NA        NA   NA
##    5:       NA          NA        NA   NA
##   ---                                    
## 1584:       NA          NA        NA   NA
## 1585:       NA          NA        NA   NA
## 1586:       NA          NA        NA   NA
## 1587:       NA          NA        NA   NA
## 1588: 3.011107 -0.27651024 0.7821562   NA
resdt.dds.Narrow.Broad.d0[, Significant := padj < alpha]
resdt.dds.Narrow.Broad.d0[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##  1:  ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  2:  ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  3:  CCGAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGACGGTTGATTAAGTCTGATGTGAAAGCCCGGAGCTCAACTCCGGAAAGGCATTGGAAACTGGTCAACTTGAGTGCAGTAGAGGTAAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGCGGCGAAGGCGGCTTACTGGACTGTAACTGACGTTGAGGCTCGAAAGTGTGGGT
##  4:   CCGAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTAGATAAGTCTGAAGTTAAAGGCTGTGGCTTAACCATAGTACGCTTTGGAAACTGTTTAACTTGAGTGCAGAAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
##  5:  CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT
##  6:  CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAATTGATACTGGCAGTCTTGAGTACAGTTGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTAACCTGTAACTGACATTGATGCTCGAAAGTGTGGGT
##  7:  CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGACAGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCTGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGT
##  8:   CCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTGATAAGTCTGAAGTTAAAGGCTGTGGCTCAACCATAGTTCGCTTTGGAAACTGTCAAACTTGAGTGCAGAAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
##  9:   CCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGTGGTTTATTAAGTCTGGTGTAAAAGGCAGTGGCTCAACCATTGTATGCATTGGAAACTGGTAGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGCCTGTAACTGACACTGAGGCTCGAAAGCGTGGGG
## 10:  CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 11:  CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 12:  CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCTGGAGATTAAGTGTGTTGTGAAATGTAGACGCTCAACGTCTGACTTGCAGCGCATACTGGTTTCCTTGAGTACGCACAACGTTGGCGGAATTCGTCGTGTAGCGGTGAAATGCTTAGATATGACGAAGAACTCCGATTGCGAAGGCAGCTGACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## 13:  GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT
## 14:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 15:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 16:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 17:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTTGAAACTGGCAGGCTTGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 18:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGATTAAGTCAGATGTGAAATCCCCGAGCTTAACTTGGGAACTGCATTTGAAACTGGTCAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 19:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 20:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTTGAAACTGGCAAGCTTGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 21:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCGCTAAGACAGATGTGAAATCCCCGGGCTTAACCTGGGAACTGCATTTGTGACTGGCGGGCTAGAGTATGGCAGAGGGGGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGTGGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGCCAATACTGACGCTCATGCACGAAAGCGTGGGG
## 22:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG
## 23:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTTAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG
## 24:  GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGATAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATATATGACTGCAGGACTTGAGTTTGTCAGAGGAGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGATATGTGGAAGAACACCGATGGCGAAGGCAGCCCTCTGGGACATGACTGACGCTCATGCACGAAAGCGTGGGG
## 25:  GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG
## 26:  GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 27:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG
## 28:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 29:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCAAGGCAAGTCTGAAGTGAAAGCCCGGTGCTTAACGCCGGGACTGCTTTGGAAACTGTTTGGCTGGAGTGCCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG
## 30:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAGCCCGGGGCTTAACCCCGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG
## 31:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCTGTGCAAGTCTGAAGTGAAAGGCATGGGCTCAACCTGTGGACTGCTTTGGAAACTGTGCAGCTAGAGTGTCGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATGACTGACGTTGAGGCTCGAAAGCGTGGGG
## 32:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTAAAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTTAACTAGAGTGCTGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG
## 33:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGGATTGCAAGTCAGATGTGAAAACTGGGGGCTCAACCTCCAGCCTGCATTTGAAACTGTAGTTCTTGAGTGCTGGAGAGGCAATCGGAATTCCGTGTGTAGCGGTGAAATGCGTAGATATACGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACAGTAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 34:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGCAAGGCAAGTCTGAAGTGAAAATCCGGGGCTCAACCCCGGAACTGCTTTGGAAACTGTTTAGCTGGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG
## 35:  GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG
## 36:  GCAAGCGTTATCCGGATTTATTGGGCGTAAAGAGAGTGCAGGCGGTTTTCTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGAAGTGCATCGGAAACTGGATAACTTGAGTGCAGAAGAGGGTAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTACCTGGTCTGCAACTGACGCTGAGACTCGAAAGCATGGGT
## 37:  GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTCTTTTAAGTCTAATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATTGGAAACTGGGAGACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 38:  GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGT
## 39:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGGCTTTTAAGTCTGACGTGAAAATGCGGGGCTTAACCCCGTATGGCGTTGGATACTGGAAGTCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT
## 40:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATCAGTCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTGCTGATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 41:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATCAGTTAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTGCTGATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 42:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATTGGTCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTACCAATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 43:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATTGGTCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTGCCAATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 44:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCCGTGCAAGTCCATCTTAAAAGCGTGGGGCTTAACCCCATGAGGGGATGGAAACTGCAGGGCTGGAGTGTCGGAGGGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTAGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 45:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 46:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGTTTCATAAGTCTGTCTTAAAAGTGCGGGGCTTAACCCCGTGAGGGGATGGAAACTATGGAACTGGAGTATCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 47:  GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGTGGTTTAATAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAGGGTCATTGGAAACTGTTAAACTTGAGTGCAGGAGAGAAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTTTGGCCTGTAACTGACACTGAGGCGCGAAAGCGTGGGG
## 48:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT
## 49:   GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCGCGCAGGCGGCGTCGTAAGTCGGTCTTAAAAGTGCGGGGCTTAACCCCGTGAGGGGACCGAAACTGCGATGCTAGAGTATCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAAGCGGCTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 50:  GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCGCGCAGGCGGCTTCTTAAGTCTGTCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGGGAAGCTCAGAGTATCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAAGCGGCTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 51:  GCAAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATTTTTAAGTGAGATGTGAAATACCCGGGCTTAACTTGGGTGCTGCATTTCAAACTGGAAGTCTAGAGTGCAGGAGAGGAGAAGGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGCTTCTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 52:  GCAAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATTTTTAAGTGGGATGTGAAATACCCGGGCTCAACCTGGGTGCTGCATTCCAAACTGGAAATCTAGAGTGCAGGAGGGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 53:  GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGCAGCCGGGTCTGCAAGTCAGATGTGAAATCCATGGGCTCAACCCATGAACTGCATTTGAAACTGTAGATCTTGAGTGTCGGAGGGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGATAACTGACGGTGAGGCGCGAAAGTGTGGGG
## 54:  GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT
## 55:  GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAAGAATAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAACTGCATCGGAAACTGTTTTTCTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 56:  GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAATGATAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAACTGCATCGGAAACTGTCATTCTTGAGTGCAGAAGAGGAGAGTGGAATTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 57:  GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAGAAATAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAACTGCATCGGAAACTGTTTTTCTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 58:  GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCCCCGGCTCAACCGGGGAGGGTCATTGGAAACTGGGAGACTTGAGTGCAGAAGAGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 59:  GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGT
## 60:  GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGTATGGGT
## 61:  GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT
## 62:  GCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTGGGTAAGACAGATGTGAAATCCCCGGGCTTAACCTGGGAACTGCATTTGTGACTGTCCGACTGGAGTATGTCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCGATGGCGAAGGCAGCCCCCTGGGGCAAAACTGACGCTCATGCACGAAAGCGTGGGG
## 63:  GCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTGTGTAAGACAGATGTGAAATGCCCGGGCTTAACCTGGGAATTGCATTTGTGACTGCACGGCTAGAGTGTGTCAGAGGGGGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGATATGTGGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGATAACACTGACGCTCATGCACGAAAGCGTGGGG
## 64:  GCGAGCGTTAATCGGAATTACTGGGCGTAAAGGGTGCGCAGGCGGTTGAGTAAGACAGATGTGAAATCCCCGAGCTTAACTCGGGAATGGCATATGTGACTGCTCGACTAGAGTGTGTCAGAGGGAGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGATATGTGGAAGAACACCGATGGCGAAGGCAGCCTCCTGGGACATAACTGACGCTCAGGCACGAAAGCGTGGGG
## 65:  GCGAGCGTTAATCGGATTTACTGGGCGTAAAGCGTGCGTAGGCGGCTTTTTAAGTCGGATGTGAAATCCCTGAGCTTAACTTAGGAATTGCATTCGATACTGGGAAGCTAGAGTATGGGAGAGGATGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGATGGCGAAGGCAGCCATCTGGCCTAATACTGACGCTGAGGTACGAAAGCATGGGG
## 66:  GCGAGCGTTATCCGGAATTATTGGGCGTAAAGAGCGCGCAGGTGGTTGATTAAGTCTGATGTGAAAGCCCACGGCTTAACCGTGGAGGGTCATTGGAAACTGGTCGACTTGAGTGCAGAAGAGGGAAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGAGATATGGAGGAACACCAGTGGCGAAGGCGGCTTCCTGGTCTGTAACTGACACTGAGGCGCGAAAGCGTGGGG
## 67:   GCGAGCGTTATCCGGAATTATTGGGCGTAAAGAGGGAGCAGGCGGCACTAAGGGTCTGTGGTGAAAGATCGAAGCTTAACTTCGGTAAGCCATGGAAACCGTAGAGCTAGAGTGTGTGAGAGGATCGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAGGCGACGATCTGGCGCATAACTGACGCTCAGTCCCGAAAGCGTGGGG
## 68:  GCGAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGTAGGCGGGAATGCAAGTCAGATGTGAAATCCAGGGGCTTAACCCTTGAACTGCATTTGAAACTGTATTTCTTGAGTGTCGGAGAGGTTGACGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGTCAACTGGACGATAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 69:  GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGGGATTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGTTCCCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCATAGATATCACGAAGAACCCCGATTGCGAAGGCAGCCTGCTAAGCTGTAACTGACGTTGAGGCTCGAAAGTGTGGGT
## 70:  GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGTGGTGATTTAAGTCAGCGGTGAAAGTTTGTGGCTCAACCATAAAATTGCCGTTGAAACTGGGTTACTTGAGTGTGTTTGAGGTAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACCATAACTGACACTGAAGCACGAAAGCGTGGGG
## 71:   GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT
## 72: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTTGTAGGCGGTTTGTCGCGTCTGCTGTGAAAGGCCGGAGCTTAACTCCGTGTATTGCAGTGGGTACGGGCAGACTAGAGTGCAGTAGGGGAGACTGGAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAACACCGATGGCGAAGGCAGGTCTCTGGGCTGTAACTGACGCTGAGAAGCGAAAGCATGGGG
## 73: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTTGTAGGCGGTTTGTCGCGTCTGCTGTGAAAGGCCGGGGCTTAACTCCGTGTATTGCAGTGGGTACGGGCAGACTAGAGTGCAGTAGGGGAGACTGGAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAACACCGATGGCGAAGGCAGGTCTCTGGGCTGTAACTGACGCTGAGAAGCGAAAGCATGGGG
## 74:   GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAGGTAAGTCTATCTTAAAAGTGCGGGGCTCAACCCCGTGAGGGGATGGAAACTATCTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 75:   GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAGGTCAAGTCTATCTTAAAAGTGCGGGGCTCAACCCCGTGAGGGGATGGAAACTGGTCTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 76:  GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCTTGTAGGCGGTTGGTCGCGTCTGCCGTGAAATCCTCTGGCTTAACTGGGGGCGTGCGGTGGGTACGGGCTGACTTGAGTGCGGTAGGGGAGACTGGAACTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAAGAACACCGGTGGCGAAGGCGGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 77:  GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCTTGTAGGCGGTTGGTCGCGTCTGCCGTGAAATTCTCTGGCTTAACTGGGGGCGTGCGGTGGGTACGGGCTGACTTGAGTGCGGTAGGGGAGACTGGAACTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAAGAACACCGGTGGCGAAGGCGGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 78:  GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCTTGTAGGCGGTTTGTCGCGTCTGCCGTGAAATCCTCTGGCTTAACTGGGGGCGTGCGGTGGGTACGGGCAGGCTTGAGTGCGGTAGGGGAGACTGGAACTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAAGAACACCGGTGGCGAAGGCGGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 79:  GCGAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGACTTTTAAGTGAGATGTGAAATACCCGGGCTCAACTTGGGTGCTGCATTTCAAACTGGAAGTCTAGAGTGCAGGAGAGGAGAATGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGATTCTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 80:  GCGAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATTTTTAAGTGGGATGTGAAATACCCGGGCTCAACCTGGGTGCTGCATTCCAAACTGGGAATCTAGAGTGCAGGAGGGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGACTCTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 81:  GCGAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGACCAGTAAGTCTGTCGTCAAAGGCGGAGGCTCAACCTTCGTTCCACGATAGATACTGCGGGTCTAGAGTATGTGAGAGGGAAGTGGAATTCCCGGTGTAGCGGTGAAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCTTCCTGGCACACAACTGACGCTCATGTGCGAAAGCCAGGGC
## 82:   GCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 83:   GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 84:  TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
##                                                                                                                                                                                                                                            OTU
##      Kingdom          Phylum               Class              Order
##  1: Bacteria      Firmicutes          Clostridia      Clostridiales
##  2: Bacteria      Firmicutes          Clostridia      Clostridiales
##  3: Bacteria      Firmicutes             Bacilli    Lactobacillales
##  4: Bacteria      Firmicutes             Bacilli    Lactobacillales
##  5: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
##  6: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
##  7: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
##  8: Bacteria      Firmicutes             Bacilli    Lactobacillales
##  9: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 10: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
## 11: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
## 12: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
## 13: Bacteria  Proteobacteria Deltaproteobacteria Desulfovibrionales
## 14: Bacteria  Proteobacteria Gammaproteobacteria  Enterobacteriales
## 15: Bacteria  Proteobacteria Gammaproteobacteria  Enterobacteriales
## 16: Bacteria  Proteobacteria Gammaproteobacteria  Enterobacteriales
## 17: Bacteria  Proteobacteria Gammaproteobacteria  Enterobacteriales
## 18: Bacteria  Proteobacteria Gammaproteobacteria  Enterobacteriales
## 19: Bacteria  Proteobacteria Gammaproteobacteria  Enterobacteriales
## 20: Bacteria  Proteobacteria Gammaproteobacteria  Enterobacteriales
## 21: Bacteria  Proteobacteria  Betaproteobacteria    Burkholderiales
## 22: Bacteria  Proteobacteria  Betaproteobacteria    Burkholderiales
## 23: Bacteria  Proteobacteria  Betaproteobacteria    Burkholderiales
## 24: Bacteria  Proteobacteria  Betaproteobacteria    Burkholderiales
## 25: Bacteria  Actinobacteria      Actinobacteria  Bifidobacteriales
## 26: Bacteria  Actinobacteria      Actinobacteria  Bifidobacteriales
## 27: Bacteria      Firmicutes          Clostridia      Clostridiales
## 28: Bacteria      Firmicutes          Clostridia      Clostridiales
## 29: Bacteria      Firmicutes          Clostridia      Clostridiales
## 30: Bacteria      Firmicutes          Clostridia      Clostridiales
## 31: Bacteria      Firmicutes          Clostridia      Clostridiales
## 32: Bacteria      Firmicutes          Clostridia      Clostridiales
## 33: Bacteria      Firmicutes          Clostridia      Clostridiales
## 34: Bacteria      Firmicutes          Clostridia      Clostridiales
## 35: Bacteria      Firmicutes          Clostridia      Clostridiales
## 36: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 37: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 38: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 39: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 40: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 41: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 42: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 43: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 44: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 45: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 46: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 47: Bacteria      Firmicutes             Bacilli         Bacillales
## 48: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 49: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 50: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 51: Bacteria      Firmicutes          Clostridia      Clostridiales
## 52: Bacteria      Firmicutes          Clostridia      Clostridiales
## 53: Bacteria      Firmicutes          Clostridia      Clostridiales
## 54: Bacteria      Firmicutes          Clostridia      Clostridiales
## 55: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 56: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 57: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 58: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 59: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 60: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 61: Bacteria      Firmicutes             Bacilli    Lactobacillales
## 62: Bacteria  Proteobacteria  Betaproteobacteria    Burkholderiales
## 63: Bacteria  Proteobacteria  Betaproteobacteria    Burkholderiales
## 64: Bacteria  Proteobacteria  Betaproteobacteria    Burkholderiales
## 65: Bacteria  Proteobacteria Gammaproteobacteria    Pseudomonadales
## 66: Bacteria      Firmicutes    Erysipelotrichia Erysipelotrichales
## 67: Bacteria      Firmicutes    Erysipelotrichia Erysipelotrichales
## 68: Bacteria      Firmicutes          Clostridia      Clostridiales
## 69: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
## 70: Bacteria   Bacteroidetes         Bacteroidia      Bacteroidales
## 71: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 72: Bacteria  Actinobacteria      Actinobacteria    Actinomycetales
## 73: Bacteria  Actinobacteria      Actinobacteria    Actinomycetales
## 74: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 75: Bacteria      Firmicutes       Negativicutes    Selenomonadales
## 76: Bacteria  Actinobacteria      Actinobacteria    Actinomycetales
## 77: Bacteria  Actinobacteria      Actinobacteria    Actinomycetales
## 78: Bacteria  Actinobacteria      Actinobacteria    Actinomycetales
## 79: Bacteria      Firmicutes          Clostridia      Clostridiales
## 80: Bacteria      Firmicutes          Clostridia      Clostridiales
## 81: Bacteria   Synergistetes         Synergistia      Synergistales
## 82: Bacteria      Firmicutes          Clostridia      Clostridiales
## 83: Bacteria      Firmicutes          Clostridia      Clostridiales
## 84: Bacteria Verrucomicrobia    Verrucomicrobiae Verrucomicrobiales
##      Kingdom          Phylum               Class              Order
##                           Family                     Genus
##  1:              Ruminococcaceae          Faecalibacterium
##  2:              Ruminococcaceae          Faecalibacterium
##  3:             Leuconostocaceae               Leuconostoc
##  4:             Streptococcaceae             Streptococcus
##  5:               Bacteroidaceae               Bacteroides
##  6:               Bacteroidaceae               Bacteroides
##  7:               Bacteroidaceae               Bacteroides
##  8:             Streptococcaceae             Streptococcus
##  9:             Streptococcaceae               Lactococcus
## 10:               Prevotellaceae                Prevotella
## 11:               Prevotellaceae                Prevotella
## 12:               Prevotellaceae                Prevotella
## 13:          Desulfovibrionaceae                 Bilophila
## 14:           Enterobacteriaceae               Citrobacter
## 15:           Enterobacteriaceae              Enterobacter
## 16:           Enterobacteriaceae                      <NA>
## 17:           Enterobacteriaceae                      <NA>
## 18:           Enterobacteriaceae                    Hafnia
## 19:           Enterobacteriaceae      Escherichia/Shigella
## 20:           Enterobacteriaceae                      <NA>
## 21:             Burkholderiaceae              Burkholderia
## 22:               Sutterellaceae                Sutterella
## 23:               Sutterellaceae                Sutterella
## 24:               Sutterellaceae                Sutterella
## 25:           Bifidobacteriaceae           Bifidobacterium
## 26:           Bifidobacteriaceae           Bifidobacterium
## 27:              Ruminococcaceae            Clostridium_IV
## 28:              Lachnospiraceae                 Roseburia
## 29:              Lachnospiraceae          Fusicatenibacter
## 30:              Lachnospiraceae          Clostridium_XlVa
## 31:              Lachnospiraceae                     Dorea
## 32:              Lachnospiraceae          Clostridium_XlVa
## 33:              Ruminococcaceae            Flavonifractor
## 34:              Lachnospiraceae               Coprococcus
## 35:              Lachnospiraceae               Coprococcus
## 36:             Lactobacillaceae             Lactobacillus
## 37:             Lactobacillaceae               Pediococcus
## 38:             Lactobacillaceae             Lactobacillus
## 39:           Acidaminococcaceae           Acidaminococcus
## 40:              Veillonellaceae               Veillonella
## 41:              Veillonellaceae               Veillonella
## 42:              Veillonellaceae               Veillonella
## 43:              Veillonellaceae               Veillonella
## 44:              Veillonellaceae               Allisonella
## 45:              Veillonellaceae                 Dialister
## 46:              Veillonellaceae               Veillonella
## 47: Bacillales_Incertae_Sedis_XI                   Gemella
## 48:              Veillonellaceae                 Megamonas
## 49:              Veillonellaceae               Megasphaera
## 50:              Veillonellaceae               Megasphaera
## 51:             Clostridiaceae_1 Clostridium_sensu_stricto
## 52:             Clostridiaceae_1 Clostridium_sensu_stricto
## 53:              Ruminococcaceae                      <NA>
## 54:              Ruminococcaceae              Ruminococcus
## 55:             Lactobacillaceae             Lactobacillus
## 56:             Lactobacillaceae             Lactobacillus
## 57:             Lactobacillaceae             Lactobacillus
## 58:              Enterococcaceae              Enterococcus
## 59:             Lactobacillaceae             Lactobacillus
## 60:             Lactobacillaceae             Lactobacillus
## 61:             Lactobacillaceae             Lactobacillus
## 62:               Sutterellaceae                      <NA>
## 63:             Oxalobacteraceae                      <NA>
## 64:               Sutterellaceae            Parasutterella
## 65:                Moraxellaceae             Acinetobacter
## 66:          Erysipelotrichaceae              Turicibacter
## 67:          Erysipelotrichaceae         Clostridium_XVIII
## 68:              Ruminococcaceae                      <NA>
## 69:               Bacteroidaceae               Bacteroides
## 70:           Porphyromonadaceae           Parabacteroides
## 71:           Acidaminococcaceae     Phascolarctobacterium
## 72:               Micrococcaceae                    Rothia
## 73:               Micrococcaceae                    Rothia
## 74:              Veillonellaceae               Mitsuokella
## 75:              Veillonellaceae               Mitsuokella
## 76:             Actinomycetaceae               Actinomyces
## 77:             Actinomycetaceae               Actinomyces
## 78:             Actinomycetaceae               Actinomyces
## 79:             Clostridiaceae_1 Clostridium_sensu_stricto
## 80:             Clostridiaceae_1 Clostridium_sensu_stricto
## 81:               Synergistaceae            Cloacibacillus
## 82:        Peptostreptococcaceae                Romboutsia
## 83:        Peptostreptococcaceae           Intestinibacter
## 84:          Verrucomicrobiaceae               Akkermansia
##                           Family                     Genus
##               Species     baseMean log2FoldChange    lfcSE         stat
##  1:       prausnitzii    3.1390305      5.9442601 2.973056   1.99937704
##  2:       prausnitzii   10.1113002      7.6305860 2.591135   2.94488136
##  3:              <NA>    0.5670333      3.5189396 3.014336   1.16740129
##  4:              <NA>    6.8415384      4.5388080 2.945712   1.54081893
##  5:              <NA>    7.2662289      2.4404837 2.162371   1.12861475
##  6:      massiliensis    1.2401237      4.5896686 2.988136   1.53596390
##  7:              <NA>    2.0123037      2.2442982 2.955671   0.75931934
##  8:              <NA>  171.2643089      1.0820588 1.224416   0.88373453
##  9:              <NA>    6.8013088      2.3304887 2.367147   0.98451384
## 10:             copri   76.9153769     -2.5117179 2.661498  -0.94372349
## 11:              <NA>    2.5451127      1.2458367 2.941859   0.42348612
## 12:         histicola    0.6319000     -1.6624347 2.998334  -0.55445274
## 13:       wadsworthia  268.8317952     -8.5594919 1.565761  -5.46666703
## 14:              <NA>    8.5239695    -21.2166263 2.982198  -7.11442686
## 15:              <NA>  128.0705643     -5.5989889 2.455047  -2.28060392
## 16:              <NA> 1956.7376403     -8.5447507 1.418316  -6.02457451
## 17:              <NA>    1.2221755     -2.6534371 2.989453  -0.88759963
## 18:             alvei    0.9006996     -2.2069847 2.992726  -0.73744968
## 19:              <NA>  604.4929876     -3.9728330 1.261105  -3.15027844
## 20:              <NA>    0.7467910     -1.9083905 2.995543  -0.63707664
## 21:              <NA>    1.1220941      4.4458211 2.990718   1.48653953
## 22:     stercoricanis  155.9441517    -25.4250205 2.504133 -10.15322158
## 23:              <NA>   24.0180682    -22.8642206 2.813303  -8.12717947
## 24:    wadsworthensis   67.7843959     -8.4408338 2.117932  -3.98541349
## 25:              <NA>    0.6617053      3.6764425 3.009770   1.22150261
## 26:              <NA>    4.4294496      2.9712738 2.091317   1.42076713
## 27:              <NA>    0.6483125      3.4342201 3.016796   1.13836651
## 28:              <NA>    5.4676945      4.0882424 2.774161   1.47368623
## 29:              <NA>    0.5514211      3.4342201 3.016796   1.13836651
## 30:              <NA>    0.8318677      4.0237852 2.999934   1.34129107
## 31:   formicigenerans    0.7939249      3.9714865 3.001272   1.32326795
## 32:              <NA>    0.7957836      1.3884862 2.999945   0.46283728
## 33:           plautii    1.2404000      4.5997366 2.987964   1.53942156
## 34:              <NA>    0.9155733      4.1591661 2.996685   1.38792215
## 35:              <NA>    1.1669626      4.4937078 2.989830   1.50299759
## 36:              <NA>    0.6617053      3.6764425 3.009770   1.22150261
## 37:              <NA>  195.1423429      9.3574474 2.615391   3.57783930
## 38:              <NA>   44.9791388     -7.8484046 2.435579  -3.22239756
## 39:              <NA>   20.6753613     -3.5485640 2.935158  -1.20898570
## 40:              <NA>    0.6405347     -1.6814551 2.998101  -0.56083997
## 41:              <NA>    1.5866839     -3.0284352 2.987380  -1.01374285
## 42:              <NA>    1.2865005     -2.7270986 2.989002  -0.91237754
## 43:              <NA>   55.0316077     -6.2673007 2.597626  -2.41270311
## 44:  histaminiformans    0.8891927     -2.2100932 2.992699  -0.73849491
## 45:              <NA>    2.2136640      2.7777882 2.726675   1.01874558
## 46:              <NA>   13.3273462    -21.9810648 2.981726  -7.37192755
## 47:              <NA>    3.2785365      4.6696428 2.267994   2.05893107
## 48:        funiformis  233.2355987     -7.6497327 2.945067  -2.59747362
## 49:              <NA>    0.6237367      3.6428814 3.010734   1.20996463
## 50:   micronuciformis   89.0482161     -4.7614791 2.307817  -2.06319593
## 51:     cellulovorans    0.5670333      3.5189396 3.014336   1.16740129
## 52:    paraputrificum    0.8108166     -2.0654478 2.993990  -0.68986467
## 53:              <NA>    2.0296074      1.5432424 2.758483   0.55945320
## 54:              <NA>    1.2432599      2.0627999 2.977146   0.69287843
## 55:              <NA>    0.7869974      3.9594093 3.001587   1.31910522
## 56:       delbrueckii    2.8139592     -0.2825662 2.945301  -0.09593796
## 57:        kalixensis    0.7394379      3.8533599 3.004472   1.28254137
## 58:              <NA>    0.7481220      1.6009486 3.002997   0.53311697
## 59:              <NA>    6.6148627     22.6774259 2.969346   7.63717739
## 60:              <NA>  177.3227612      2.2093872 1.713957   1.28905648
## 61:        salivarius    1.6771647     -3.1035423 2.987025  -1.03900767
## 62:              <NA>    1.0926032      4.4188393 2.991232   1.47726413
## 63:              <NA>    1.9201883     -3.2930334 2.986208  -1.10274761
## 64: excrementihominis   29.8594555     -6.2815264 2.513662  -2.49895450
## 65:              <NA>    0.7130229     -1.8843736 2.995795  -0.62900609
## 66:              <NA>    1.6768222     -3.1074859 2.677216  -1.16071550
## 67:              <NA>    0.6604966      1.4795366 3.007301   0.49198157
## 68:              <NA>    0.5545784      3.4563143 3.016258   1.14589474
## 69:              <NA>    1.2966251      4.6479013 2.987160   1.55595983
## 70:            merdae    1.0385625      4.3441712 2.992702   1.45158809
## 71:           faecium  349.3097927    -26.5334911 2.391792 -11.09356272
## 72:      mucilaginosa    0.6732565      3.7000399 3.009027   1.22964679
## 73:      mucilaginosa    2.0208216      5.3146081 2.978370   1.78440130
## 74:       jalaludinii    2.0002740     -2.6407803 2.983886  -0.88501372
## 75:              <NA>    1.5438006     -2.9890537 2.987574  -1.00049544
## 76:     odontolyticus   14.5512433      6.2353391 2.147200   2.90394011
## 77:     odontolyticus    2.4255187     -3.6338470 2.711006  -1.34040528
## 78:              <NA>    3.5719250      6.1256910 2.971906   2.06119933
## 79:              <NA>    0.5438958     -1.4808218 3.000716  -0.49348950
## 80:           baratii    0.7400080     -1.9424271 2.995192  -0.64851502
## 81:         evryensis   18.6608616     -5.8656361 2.978407  -1.96938721
## 82:              <NA>   16.2747242     -3.7583810 2.030637  -1.85083866
## 83:        bartlettii    1.0349767      0.4350350 2.986993   0.14564315
## 84:       muciniphila  634.1688355     -6.8224822 2.679427  -2.54624704
##               Species     baseMean log2FoldChange    lfcSE         stat
##           pvalue         padj Significant
##  1: 4.556757e-02 1.664207e-01       FALSE
##  2: 3.230785e-03 2.087584e-02        TRUE
##  3: 2.430484e-01 3.956559e-01       FALSE
##  4: 1.233609e-01 3.472809e-01       FALSE
##  5: 2.590604e-01 3.956559e-01       FALSE
##  6: 1.245472e-01 3.472809e-01       FALSE
##  7: 4.476615e-01 5.610335e-01       FALSE
##  8: 3.768395e-01 4.796140e-01       FALSE
##  9: 3.248629e-01 4.473522e-01       FALSE
## 10: 3.453110e-01 4.678407e-01       FALSE
## 11: 6.719406e-01 6.883294e-01       FALSE
## 12: 5.792690e-01 6.319299e-01       FALSE
## 13: 4.585763e-08 4.815051e-07        TRUE
## 14: 1.123788e-12 1.573304e-11        TRUE
## 15: 2.257190e-02 9.979154e-02       FALSE
## 16: 1.695551e-09 2.034661e-08        TRUE
## 17: 3.747562e-01 4.796140e-01       FALSE
## 18: 4.608489e-01 5.610335e-01       FALSE
## 19: 1.631149e-03 1.141804e-02        TRUE
## 20: 5.240749e-01 6.008782e-01       FALSE
## 21: 1.371365e-01 3.472809e-01       FALSE
## 22: 3.206019e-24 1.346528e-22        TRUE
## 23: 4.393946e-16 1.230305e-14        TRUE
## 24: 6.736272e-05 6.287187e-04        TRUE
## 25: 2.218958e-01 3.956559e-01       FALSE
## 26: 1.553845e-01 3.625638e-01       FALSE
## 27: 2.549675e-01 3.956559e-01       FALSE
## 28: 1.405661e-01 3.472809e-01       FALSE
## 29: 2.549675e-01 3.956559e-01       FALSE
## 30: 1.798260e-01 3.833964e-01       FALSE
## 31: 1.857463e-01 3.833964e-01       FALSE
## 32: 6.434810e-01 6.673136e-01       FALSE
## 33: 1.237014e-01 3.472809e-01       FALSE
## 34: 1.651608e-01 3.749595e-01       FALSE
## 35: 1.328397e-01 3.472809e-01       FALSE
## 36: 2.218958e-01 3.956559e-01       FALSE
## 37: 3.464463e-04 2.910149e-03        TRUE
## 38: 1.271226e-03 9.707545e-03        TRUE
## 39: 2.266683e-01 3.956559e-01       FALSE
## 40: 5.749066e-01 6.319299e-01       FALSE
## 41: 3.107055e-01 4.423603e-01       FALSE
## 42: 3.615700e-01 4.796140e-01       FALSE
## 43: 1.583471e-02 7.389532e-02       FALSE
## 44: 4.602138e-01 5.610335e-01       FALSE
## 45: 3.083238e-01 4.423603e-01       FALSE
## 46: 1.681784e-13 2.825397e-12        TRUE
## 47: 3.950084e-02 1.508214e-01       FALSE
## 48: 9.391233e-03 5.259091e-02       FALSE
## 49: 2.262925e-01 3.956559e-01       FALSE
## 50: 3.909402e-02 1.508214e-01       FALSE
## 51: 2.430484e-01 3.956559e-01       FALSE
## 52: 4.902793e-01 5.800487e-01       FALSE
## 53: 5.758525e-01 6.319299e-01       FALSE
## 54: 4.883858e-01 5.800487e-01       FALSE
## 55: 1.871339e-01 3.833964e-01       FALSE
## 56: 9.235698e-01 9.235698e-01       FALSE
## 57: 1.996528e-01 3.900194e-01       FALSE
## 58: 5.939526e-01 6.396413e-01       FALSE
## 59: 2.220355e-14 4.662745e-13        TRUE
## 60: 1.973785e-01 3.900194e-01       FALSE
## 61: 2.988012e-01 4.403386e-01       FALSE
## 62: 1.396048e-01 3.472809e-01       FALSE
## 63: 2.701368e-01 4.052052e-01       FALSE
## 64: 1.245603e-02 6.154744e-02       FALSE
## 65: 5.293451e-01 6.008782e-01       FALSE
## 66: 2.457576e-01 3.956559e-01       FALSE
## 67: 6.227324e-01 6.538690e-01       FALSE
## 68: 2.518387e-01 3.956559e-01       FALSE
## 69: 1.197176e-01 3.472809e-01       FALSE
## 70: 1.466162e-01 3.518788e-01       FALSE
## 71: 1.348095e-28 1.132400e-26        TRUE
## 72: 2.188294e-01 3.956559e-01       FALSE
## 73: 7.435848e-02 2.402351e-01       FALSE
## 74: 3.761492e-01 4.796140e-01       FALSE
## 75: 3.170708e-01 4.438991e-01       FALSE
## 76: 3.684986e-03 2.210992e-02        TRUE
## 77: 1.801136e-01 3.833964e-01       FALSE
## 78: 3.928403e-02 1.508214e-01       FALSE
## 79: 6.216668e-01 6.538690e-01       FALSE
## 80: 5.166519e-01 6.008782e-01       FALSE
## 81: 4.890864e-02 1.711803e-01       FALSE
## 82: 6.419277e-02 2.156877e-01       FALSE
## 83: 8.842031e-01 8.948561e-01       FALSE
## 84: 1.088881e-02 5.716625e-02       FALSE
##           pvalue         padj Significant
resdt.dds.Narrow.Broad.d0
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species  baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA> 0.0000000             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000             NA
##    3: Fusobacteriaceae Fusobacterium      <NA> 0.1663214    -0.06235141
##    4:    Rikenellaceae          <NA>      <NA> 0.0000000             NA
##    5:   Eubacteriaceae   Eubacterium      <NA> 0.0000000             NA
##   ---                                                                  
## 1584:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris 0.0000000             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA> 0.0000000             NA
## 1587:   Prevotellaceae    Prevotella      <NA> 0.0000000             NA
## 1588:   Prevotellaceae    Prevotella     bivia 0.3416447    -0.83260200
##          lfcSE        stat    pvalue padj Significant
##    1:       NA          NA        NA   NA          NA
##    2:       NA          NA        NA   NA          NA
##    3: 3.021907 -0.02063313 0.9835383   NA          NA
##    4:       NA          NA        NA   NA          NA
##    5:       NA          NA        NA   NA          NA
##   ---                                                
## 1584:       NA          NA        NA   NA          NA
## 1585:       NA          NA        NA   NA          NA
## 1586:       NA          NA        NA   NA          NA
## 1587:       NA          NA        NA   NA          NA
## 1588: 3.011107 -0.27651024 0.7821562   NA          NA
volcano.Narrow.Broad.d0 = ggplot(
  data = resdt.dds.Narrow.Broad.d0[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.Narrow.Broad.d0[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 0 Narrow Spectrum vs Broad Spectrum") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.Narrow.Broad.d0

summary(res.dds.Narrow.Broad.d0)
## 
## out of 170 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 4, 2.4%
## LFC < 0 (down)     : 15, 8.8%
## outliers [1]       : 0, 0%
## low counts [2]     : 205, 121%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.Narrow.Broad.d0, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                                        description
##                                                                                                        <character>
## baseMean                                                                 mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj                                                                                          BH adjusted p-values
ggplotly(volcano.Narrow.Broad.d0)

##Narrow vs. Broad Spectrum Antibiotics Day 7

# Differential Abundance Testing
sample_data(ps1.NvB.7)$randomization_arm
##  [1] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [3] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [5] Broad spectrum antibiotics  Narrow spectrum antibiotics
##  [7] Narrow spectrum antibiotics Broad spectrum antibiotics 
##  [9] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [13] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [15] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [17] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [19] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [21] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [23] Narrow spectrum antibiotics Broad spectrum antibiotics 
## [25] Broad spectrum antibiotics  Narrow spectrum antibiotics
## [27] Narrow spectrum antibiotics Broad spectrum antibiotics 
## Levels: Narrow spectrum antibiotics Broad spectrum antibiotics
sample_data(ps1.NvB.7)$day
##  [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
ds.Narrow.Broad.d7 <- phyloseq_to_deseq2(ps1.NvB.7, ~randomization_arm)

geoMeans.ds.Narrow.Broad.d7 <- apply(counts(ds.Narrow.Broad.d7), 1, gm_mean)
ds.Narrow.Broad.d7 <- estimateSizeFactors(ds.Narrow.Broad.d7, geoMeans = geoMeans.ds.Narrow.Broad.d7)

dds.Narrow.Broad.d7 <- DESeq(ds.Narrow.Broad.d7, test="Wald", fitType="local", betaPrior = FALSE)

# Tabulate and write results
res.dds.Narrow.Broad.d7 = results(dds.Narrow.Broad.d7, cooksCutoff = FALSE)
sigtab_dds.dds.Narrow.Broad.d7 = res.dds.Narrow.Broad.d7[which(res.dds.Narrow.Broad.d7$padj < alpha), ]
sigtab_dds.dds.Narrow.Broad.d7 = cbind(as(sigtab_dds.dds.Narrow.Broad.d7, "data.frame"), as(tax_table(ps1.NvB_9)[rownames(sigtab_dds.dds.Narrow.Broad.d7), ], "matrix"))
summary(res.dds.Narrow.Broad.d7)
## 
## out of 464 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 3, 0.65%
## LFC < 0 (down)     : 15, 3.2%
## outliers [1]       : 0, 0%
## low counts [2]     : 730, 157%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
head(sigtab_dds.dds.Narrow.Broad.d7)
##                                                                                                                                                                                                                                             baseMean
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 1561.11009
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT    74.50843
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT   11.39754
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT   15.13717
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT   27.85206
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG   35.41294
##                                                                                                                                                                                                                                           log2FoldChange
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG     -11.846096
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT      -25.602786
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT     -23.045137
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT     -23.428026
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     -24.261047
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG      -6.297244
##                                                                                                                                                                                                                                              lfcSE
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 2.161968
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT  2.947715
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 2.948489
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.948263
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.893439
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG 2.152931
##                                                                                                                                                                                                                                                stat
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG -5.479312
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT  -8.685638
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT -7.815913
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -7.946383
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -8.384848
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG -2.924964
##                                                                                                                                                                                                                                                 pvalue
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 4.269838e-08
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT  3.766219e-18
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 5.456586e-15
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.920367e-15
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 5.079129e-17
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG 3.444963e-03
##                                                                                                                                                                                                                                                   padj
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 4.818817e-07
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT  5.950626e-16
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 1.231629e-13
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 7.585451e-14
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 4.012512e-15
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG 3.628694e-02
##                                                                                                                                                                                                                                            Kingdom
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Bacteria
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT  Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG Bacteria
##                                                                                                                                                                                                                                                    Phylum
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobia
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT       Firmicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT      Firmicutes
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT   Bacteroidetes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      Firmicutes
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG  Actinobacteria
##                                                                                                                                                                                                                                                      Class
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobiae
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT     Negativicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT       Clostridia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      Bacteroidia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT       Clostridia
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG   Actinobacteria
##                                                                                                                                                                                                                                                        Order
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobiales
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT     Selenomonadales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT      Clostridiales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      Bacteroidales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT      Clostridiales
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG  Bifidobacteriales
##                                                                                                                                                                                                                                                        Family
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobiaceae
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT      Veillonellaceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT     Ruminococcaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      Bacteroidaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT     Ruminococcaceae
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG  Bifidobacteriaceae
##                                                                                                                                                                                                                                                      Genus
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG      Akkermansia
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT         Megamonas
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT     Ruminococcus
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT      Bacteroides
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG  Bifidobacterium
##                                                                                                                                                                                                                                               Species
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG muciniphila
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT   funiformis
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT        <NA>
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT       dorei
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG        <NA>
write.table(sigtab_dds.dds.Narrow.Broad.d7, file="./results/deseq_d7_Narrow.Broad.txt", sep = "\t") # Change filename to save results to appropriate file

# Quick check of factor levels
mcols(res.dds.Narrow.Broad.d7, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                                        description
##                                                                                                        <character>
## baseMean                                                                 mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj                                                                                          BH adjusted p-values
# Prepare to join results data.table and taxonomy table
resdt.dds.Narrow.Broad.d7 = data.table(as(results(dds.Narrow.Broad.d7, cooksCutoff = FALSE), "data.frame"),
                   keep.rownames = TRUE)
setnames(resdt.dds.Narrow.Broad.d7, "rn", "OTU")
taxdt.dds.d7.com = data.table(data.frame(as(tax_table(ps1.NvB.7), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d7.com, "rn", "OTU")

# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d7.com, "OTU")
setkeyv(resdt.dds.Narrow.Broad.d7, "OTU")
resdt.dds.Narrow.Broad.d7 <- taxdt.dds.d7.com[resdt.dds.Narrow.Broad.d7]
resdt.dds.Narrow.Broad.d7
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA>        0             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum        0              0
##    3: Fusobacteriaceae Fusobacterium      <NA>        0             NA
##    4:    Rikenellaceae          <NA>      <NA>        0             NA
##    5:   Eubacteriaceae   Eubacterium      <NA>        0              0
##   ---                                                                 
## 1584:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris        0             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1587:   Prevotellaceae    Prevotella      <NA>        0              0
## 1588:   Prevotellaceae    Prevotella     bivia        0             NA
##       lfcSE stat pvalue padj
##    1:    NA   NA     NA   NA
##    2:     0    0      1   NA
##    3:    NA   NA     NA   NA
##    4:    NA   NA     NA   NA
##    5:     0    0      1   NA
##   ---                       
## 1584:    NA   NA     NA   NA
## 1585:    NA   NA     NA   NA
## 1586:    NA   NA     NA   NA
## 1587:     0    0      1   NA
## 1588:    NA   NA     NA   NA
resdt.dds.Narrow.Broad.d7[, Significant := padj < alpha]
resdt.dds.Narrow.Broad.d7[!is.na(Significant)]
##                                                                                                                                                                                                                                            OTU
##   1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##   5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##  ---                                                                                                                                                                                                                                          
## 154:  GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTCAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGGAGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 155:  GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 156: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 157: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 158: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
##       Kingdom          Phylum            Class              Order
##   1: Bacteria      Firmicutes       Clostridia      Clostridiales
##   2: Bacteria      Firmicutes       Clostridia      Clostridiales
##   3: Bacteria      Firmicutes       Clostridia      Clostridiales
##   4: Bacteria      Firmicutes       Clostridia      Clostridiales
##   5: Bacteria      Firmicutes       Clostridia      Clostridiales
##  ---                                                             
## 154: Bacteria      Firmicutes       Clostridia      Clostridiales
## 155: Bacteria      Firmicutes       Clostridia      Clostridiales
## 156: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 157: Bacteria   Bacteroidetes      Bacteroidia      Bacteroidales
## 158: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
##                     Family            Genus     Species    baseMean
##   1:       Ruminococcaceae Faecalibacterium prausnitzii  301.237846
##   2:       Ruminococcaceae Faecalibacterium prausnitzii  200.774916
##   3:       Ruminococcaceae Faecalibacterium prausnitzii   16.555778
##   4:       Ruminococcaceae Faecalibacterium prausnitzii   24.045248
##   5:       Ruminococcaceae Faecalibacterium prausnitzii   27.852059
##  ---                                                               
## 154: Peptostreptococcaceae   Clostridium_XI   difficile    4.685327
## 155: Peptostreptococcaceae  Intestinibacter  bartlettii   59.763781
## 156:         Rikenellaceae        Alistipes        <NA>    3.771817
## 157:         Rikenellaceae        Alistipes  putredinis    4.891952
## 158:   Verrucomicrobiaceae      Akkermansia muciniphila 1561.110088
##      log2FoldChange    lfcSE       stat       pvalue         padj
##   1:     -3.9563228 2.503057 -1.5805965 1.139703e-01 3.865438e-01
##   2:     -1.6000654 1.888829 -0.8471201 3.969282e-01 7.938563e-01
##   3:     -2.3993039 2.318400 -1.0348965 3.007172e-01 6.599073e-01
##   4:     -1.1036476 2.897566 -0.3808878 7.032865e-01 9.183410e-01
##   5:    -24.2610472 2.893439 -8.3848481 5.079129e-17 4.012512e-15
##  ---                                                             
## 154:      0.5088610 2.908695  0.1749448 8.611230e-01 9.721283e-01
## 155:     -0.6545537 1.109745 -0.5898234 5.553091e-01 8.127672e-01
## 156:     -2.0639050 2.921048 -0.7065633 4.798379e-01 8.127672e-01
## 157:     -5.2204808 2.949606 -1.7698907 7.674535e-02 3.071502e-01
## 158:    -11.8460961 2.161968 -5.4793116 4.269838e-08 4.818817e-07
##      Significant
##   1:       FALSE
##   2:       FALSE
##   3:       FALSE
##   4:       FALSE
##   5:        TRUE
##  ---            
## 154:       FALSE
## 155:       FALSE
## 156:       FALSE
## 157:       FALSE
## 158:        TRUE
resdt.dds.Narrow.Broad.d7
##                                                                                                                                                                                                                                             OTU
##    1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
##    2:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
##    3:  ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
##    4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
##    5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
##   ---                                                                                                                                                                                                                                          
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
##        Kingdom        Phylum         Class           Order
##    1: Bacteria    Firmicutes    Clostridia   Clostridiales
##    2: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    3: Bacteria  Fusobacteria Fusobacteriia Fusobacteriales
##    4: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##    5: Bacteria    Firmicutes    Clostridia   Clostridiales
##   ---                                                     
## 1584: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1585: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1586: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1587: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
## 1588: Bacteria Bacteroidetes   Bacteroidia   Bacteroidales
##                 Family         Genus   Species baseMean log2FoldChange
##    1:  Ruminococcaceae          <NA>      <NA>        0             NA
##    2: Fusobacteriaceae Fusobacterium nucleatum        0              0
##    3: Fusobacteriaceae Fusobacterium      <NA>        0             NA
##    4:    Rikenellaceae          <NA>      <NA>        0             NA
##    5:   Eubacteriaceae   Eubacterium      <NA>        0              0
##   ---                                                                 
## 1584:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1585:   Bacteroidaceae   Bacteroides stercoris        0             NA
## 1586:   Bacteroidaceae   Bacteroides      <NA>        0             NA
## 1587:   Prevotellaceae    Prevotella      <NA>        0              0
## 1588:   Prevotellaceae    Prevotella     bivia        0             NA
##       lfcSE stat pvalue padj Significant
##    1:    NA   NA     NA   NA          NA
##    2:     0    0      1   NA          NA
##    3:    NA   NA     NA   NA          NA
##    4:    NA   NA     NA   NA          NA
##    5:     0    0      1   NA          NA
##   ---                                   
## 1584:    NA   NA     NA   NA          NA
## 1585:    NA   NA     NA   NA          NA
## 1586:    NA   NA     NA   NA          NA
## 1587:     0    0      1   NA          NA
## 1588:    NA   NA     NA   NA          NA
volcano.Narrow.Broad.d7 = ggplot(
  data = resdt.dds.Narrow.Broad.d7[!is.na(Significant)][(pvalue < 1)],
  mapping = aes(x = log2FoldChange,
                y = -log10(pvalue),
                color = Phylum,
                label = OTU, label1 = Genus)) +
  theme_bw() +
  geom_point() + 
  geom_point(data = resdt.dds.Narrow.Broad.d7[(Significant)], size = 7, alpha = 0.7) + 
  # geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
  theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
  geom_hline(yintercept = -log10(alpha)) +
  ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 7 Narrow Spectrum vs Broad Spectrum") +
  theme(axis.title = element_text(size=12)) +
  theme(axis.text = element_text(size=12)) +
  theme(legend.text = element_text(size=12)) +
  geom_vline(xintercept = 0, lty = 2)
volcano.Narrow.Broad.d7

summary(res.dds.Narrow.Broad.d7)
## 
## out of 464 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 3, 0.65%
## LFC < 0 (down)     : 15, 3.2%
## outliers [1]       : 0, 0%
## low counts [2]     : 730, 157%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
mcols(res.dds.Narrow.Broad.d7, use.names = TRUE)
## DataFrame with 6 rows and 2 columns
##                        type
##                 <character>
## baseMean       intermediate
## log2FoldChange      results
## lfcSE               results
## stat                results
## pvalue              results
## padj                results
##                                                                                                        description
##                                                                                                        <character>
## baseMean                                                                 mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE                  standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat                   Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue              Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj                                                                                          BH adjusted p-values
ggplotly(volcano.Narrow.Broad.d7)

## Enrich and compile deseq2 results tables

# Control vs. Narrow D_9
nrow(res.dds.NoAbx.Narrow.d_9)
## [1] 1588
df1 <- as.data.frame(res.dds.NoAbx.Narrow.d_9[ which(res.dds.NoAbx.Narrow.d_9$padj < 0.05), ])
nrow(df1)
## [1] 6
df1 <- as.data.frame(df1[ which(df1$baseMean > 100), ])
nrow(df1)
## [1] 0
df1 <- rownames_to_column(df1, var = "ASV")
# df1$Comparison <- "Control_v_Narrow_d_9" errors out as there are no taxa
write.table(df1, file = "./Results/df1.txt", sep = "\t")

# Control vs. Narrow D0
nrow(res.dds.NoAbx.Narrow.d0)
## [1] 1588
df2 <- as.data.frame(res.dds.NoAbx.Narrow.d0[ which(res.dds.NoAbx.Narrow.d0$padj < 0.05), ])
nrow(df2)
## [1] 115
df2 <- as.data.frame(df2[ which(df2$baseMean > 100), ])
nrow(df2)
## [1] 21
df2 <- rownames_to_column(df2, var = "ASV")
df2$Comparison <- "Control_v_Narrow_d0"
write.table(df2, file = "./Results/df2.txt", sep = "\t")

# Control vs. Narrow D7
nrow(res.dds.NoAbx.Narrow.d7)
## [1] 1588
df3 <- as.data.frame(res.dds.NoAbx.Narrow.d7[ which(res.dds.NoAbx.Narrow.d7$padj < 0.05), ])
nrow(df3)
## [1] 11
df3 <- as.data.frame(df3[ which(df3$baseMean > 100), ])
nrow(df3)
## [1] 0
df3 <- rownames_to_column(df3, var = "ASV")
# df3$Comparison <- "Control_v_Narrow_d7" errors out as there are no taxa
write.table(df3, file = "./Results/df3.txt", sep = "\t")

# Control vs. Broad D_9
nrow(res.dds.NoAbx.Broad.d_9)
## [1] 1588
df4 <- as.data.frame(res.dds.NoAbx.Broad.d_9[ which(res.dds.NoAbx.Broad.d_9$padj < 0.05), ])
nrow(df4)
## [1] 9
df4 <- as.data.frame(df4[ which(df4$baseMean > 100), ])
nrow(df4)
## [1] 1
df4 <- rownames_to_column(df4, var = "ASV")
df4$Comparison <- "Control_v_Broad_d_9"
write.table(df4, file = "./Results/df4.txt", sep = "\t")

# Control vs. Broad D0
nrow(res.dds.NoAbx.Broad.d0)
## [1] 1588
df5 <- as.data.frame(res.dds.NoAbx.Broad.d0[ which(res.dds.NoAbx.Broad.d0$padj < 0.05), ])
nrow(df5)
## [1] 67
df5 <- as.data.frame(df5[ which(df5$baseMean > 100), ])
nrow(df5)
## [1] 11
df5 <- rownames_to_column(df5, var = "ASV")
df5$Comparison <- "Control_v_Broad_d0"
write.table(df5, file = "./Results/df5.txt", sep = "\t")

# Control vs. Broad D7
nrow(res.dds.NoAbx.Broad.d7)
## [1] 1588
df6 <- as.data.frame(res.dds.NoAbx.Broad.d7[ which(res.dds.NoAbx.Broad.d7$padj < 0.05), ])
nrow(df6)
## [1] 29
df6 <- as.data.frame(df6[ which(df6$baseMean > 100), ])
nrow(df6)
## [1] 1
df6 <- rownames_to_column(df6, var = "ASV")
df6$Comparison <- "Control_v_Broad_d7"
write.table(df6, file = "./Results/df6.txt", sep = "\t")

# Narrow vs. Broad D_9
nrow(res.dds.Narrow.Broad.d_9)
## [1] 1588
df7 <- as.data.frame(res.dds.Narrow.Broad.d_9[ which(res.dds.Narrow.Broad.d_9$padj < 0.05), ])
nrow(df7)
## [1] 11
df7 <- as.data.frame(df7[ which(df7$baseMean > 100), ])
nrow(df7)
## [1] 0
df7 <- rownames_to_column(df7, var = "ASV")
#df7$Comparison <- "Narrow_v_Broad_d_9" errors out as there are no taxa
write.table(df7, file = "./Results/df7.txt", sep = "\t")

# Narrow vs. Broad D0
nrow(res.dds.Narrow.Broad.d0)
## [1] 1588
df8 <- as.data.frame(res.dds.Narrow.Broad.d0[ which(res.dds.Narrow.Broad.d0$padj < 0.05), ])
nrow(df8)
## [1] 14
df8 <- as.data.frame(df8[ which(df8$baseMean > 100), ])
nrow(df8)
## [1] 6
df8 <- rownames_to_column(df8, var = "ASV")
df8$Comparison <- "Narrow_v_Broad_d0"
write.table(df8, file = "./Results/df8.txt", sep = "\t")

# Narrow vs. Broad D7
nrow(res.dds.Narrow.Broad.d7)
## [1] 1588
df9 <- as.data.frame(res.dds.Narrow.Broad.d7[ which(res.dds.Narrow.Broad.d7$padj < 0.05), ])
nrow(df9)
## [1] 15
df9 <- as.data.frame(df9[ which(df9$baseMean > 100), ])
nrow(df9)
## [1] 1
df9 <- rownames_to_column(df9, var = "ASV")
df9$Comparison <- "Narrow_v_Broad_d7"
write.table(df9, file = "./Results/df9.txt", sep = "\t")

# Combine all differential abundance tables
df.all <- rbind(df1, df2, df3, df4, df5, df6, df7, df8, df9)
nrow(df.all)
## [1] 41
write.table(df.all, file = "./Results/df_all.txt", sep = "\t")

# Create table of unique differentially abundant ASV
dfs <- list(df1, df2, df3, df4, df5, df6, df7, df8, df9)
df.unique <- join_all(dfs, type = "full", by = "ASV")
nrow(df.unique)
## [1] 28
write.table(df.unique, file = "./Results/df_unique.txt", sep = "\t")

Bind taxonomy to results

# Load other taxonomies preprocessed with dada2
ps1.rdp <- readRDS("./data/ps0.human_volunteer.rdp.RDS")

# Create appropirately formated taxa table
# RDP
ps1.rdp.tax <- as.tibble(as.data.frame(tax_table(ps1.rdp)))
ps1.rdp.tax <- rownames_to_column(ps1.rdp.tax, var = "ASV")
df.all.rdp <- left_join(df.all, ps1.rdp.tax, by = "ASV")
write.table(df.all.rdp, file = "./results/df_all_rdp.txt", sep = "\t")
df.unique.rdp <- left_join(df.unique, ps1.rdp.tax, by = "ASV")
write.table(df.unique.rdp, file = "./results/df_unique_rdp.txt", sep = "\t")

Individual ASV plots

##Ground truth plots
replace_counts = function(physeq, dds) {

  dds_counts = counts(dds, normalized = TRUE)
  if (!identical(taxa_names(physeq), rownames(dds_counts))) {
    stop("OTU ids don't match")
  }
  otu_table(physeq) = otu_table(dds_counts, taxa_are_rows = TRUE)
  return(physeq)

}

# Make deseq ready object
ds.all <- phyloseq_to_deseq2(ps1, ~randomization_arm)
geoMeans.all <- apply(counts(ds.all), 1, gm_mean)
ds.all <- estimateSizeFactors(ds.all, geoMeans = geoMeans.all)
dds.all <- DESeq(ds.all, test="Wald", betaPrior = TRUE)
rlog.all <- replace_counts(ps1, dds.all)
rlog.all <- psmelt(rlog.all)

# Need to change Day to numeric
class(rlog.all$day)
## [1] "integer"
rlog.all$Day <- as.numeric(as.character(rlog.all$day))
class(rlog.all$day)
## [1] "integer"
# Rename OTU to ASV
rlog.all <- rename(rlog.all, c("OTU" = "ASV"))

# Select out taxa of interest from rlog.all
rlog.all.rdp <- inner_join(df.all.rdp, rlog.all, by = "ASV")
rlog.unique.rdp <- inner_join(df.unique.rdp, rlog.all, by = "ASV")

# Note: ggplot2 will not plot smoothers for individual groups if the first smoother "fails"
# to adjust for this stat_smooth is applied to each individual groups
p.unique.rdp <- ggplot(rlog.all.rdp, aes(x = day, y = Abundance, group = randomization_arm, color = randomization_arm)) +
  geom_jitter(size = 1, alpha = 0.6, width = 0.5) +
  scale_y_log10() +
  theme(legend.position = "NULL") +
  facet_wrap(Phylum.x~Family.x) +
  stat_smooth(data = subset(rlog.all.rdp, randomization_arm == "Narrow spectrum antibiotics", method = "loess")) +
  stat_smooth(data = subset(rlog.all.rdp, randomization_arm == "Broad spectrum antibiotics", method = "loess")) +
  stat_smooth(data = subset(rlog.all.rdp, randomization_arm == "No antibiotics", method = "loess")) +
  labs(color = "Treatment", x = "Day", y = "Abundance (rlog)")
p.unique.rdp

# Combine summary plots with > 0 taxa
dfs.2 <- list(df2, df4, df5, df6, df8, df9)
df.all.2 <- join_all(dfs.2, type = "full", by = "Comparison")
nrow(df.all.2)
## [1] 41
# Bind taxonomu
df.all.rdp.2 <- left_join(df.all.2, ps1.rdp.tax, by = "ASV")

# Select from rlog normalized data
dfs.all.rdp.2 <- inner_join(df.all.2, rlog.all, by = "ASV")

# Adjust factor levels to order by time point (roughly)
df.all.rdp.2$Comparison <- as.factor(df.all.rdp.2$Comparison)
levels(df.all.rdp.2$Comparison)
## [1] "Control_v_Broad_d_9" "Control_v_Broad_d0"  "Control_v_Broad_d7" 
## [4] "Control_v_Narrow_d0" "Narrow_v_Broad_d0"   "Narrow_v_Broad_d7"
df.all.rdp.2$Comparison <- factor(df.all.rdp.2$Comparison, levels = c("Control_v_Broad_d_9", "Control_v_Narrow_d0", "Control_v_Broad_d0", "Narrow_v_Broad_d0", "Control_v_Broad_d7", "Narrow_v_Broad_d7"))
levels(df.all.rdp.2$Comparison)
## [1] "Control_v_Broad_d_9" "Control_v_Narrow_d0" "Control_v_Broad_d0" 
## [4] "Narrow_v_Broad_d0"   "Control_v_Broad_d7"  "Narrow_v_Broad_d7"
df.all.rdp.2$Comparison <- factor(df.all.rdp.2$Comparison, labels = c("Control v. Broad: Day -9", "Control v. Narrow: Day 0", "Control v. Broad: Day 0", "Broad v Narrow: Day 0", "Control v. Broad: Day 7", "Broad v. Narrow: Day 7"))
df.all.rdp.2$Comparison
##  [1] Control v. Narrow: Day 0 Control v. Narrow: Day 0
##  [3] Control v. Narrow: Day 0 Control v. Narrow: Day 0
##  [5] Control v. Narrow: Day 0 Control v. Narrow: Day 0
##  [7] Control v. Narrow: Day 0 Control v. Narrow: Day 0
##  [9] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [11] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [13] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [15] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [17] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [19] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [21] Control v. Narrow: Day 0 Control v. Broad: Day -9
## [23] Control v. Broad: Day 0  Control v. Broad: Day 0 
## [25] Control v. Broad: Day 0  Control v. Broad: Day 0 
## [27] Control v. Broad: Day 0  Control v. Broad: Day 0 
## [29] Control v. Broad: Day 0  Control v. Broad: Day 0 
## [31] Control v. Broad: Day 0  Control v. Broad: Day 0 
## [33] Control v. Broad: Day 0  Control v. Broad: Day 7 
## [35] Broad v Narrow: Day 0    Broad v Narrow: Day 0   
## [37] Broad v Narrow: Day 0    Broad v Narrow: Day 0   
## [39] Broad v Narrow: Day 0    Broad v Narrow: Day 0   
## [41] Broad v. Narrow: Day 7  
## 6 Levels: Control v. Broad: Day -9 ... Broad v. Narrow: Day 7
p.volcano.summary.rdp <- ggplot(df.all.rdp.2, aes(x = log2FoldChange, y = -log10(padj), color = Comparison, size = log10(baseMean))) +
  geom_point(alpha = 0.7) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "dark grey", lwd = 1) +
  facet_wrap(Phylum~Family, ncol = 3) +
  annotate("text", x = -15, y = 27, label = "Control") +
  annotate("text", x = 15, y = 27, label = "Treated") +
  labs(color = "Comparison") +
  labs(x=expression(log[2]*" fold-change")) +
  labs(y=expression(-log[10]*" adjusted p-value")) +
  labs(size=expression(log[10]*" base mean")) +
  scale_color_brewer(palette = "Dark2") +
  scale_y_continuous(limits = c(0,30), expand = c(0, 0)) +
  guides(colour = guide_legend(override.aes = list(size=3))) +
  theme(legend.position = "null")
p.volcano.summary.rdp

# Interctive plot
ggplotly(p.volcano.summary.rdp)
ggarrange(p.volcano.summary.rdp, p.unique.rdp, nrow = 2, labels = c("A)", "B)"), heights = c(1.3,1))

## Differential abundance for boosting at each time point

# Day -9
ds.boost.d_9 <- phyloseq_to_deseq2(ps1.d_9, ~randomization_arm + d7_rota_boost_updated)
geoMeans.ds.boost.d_9 <- apply(counts(ds.boost.d_9), 1, gm_mean)
ds.boost.d_9 <- estimateSizeFactors(ds.boost.d_9, geoMeans = geoMeans.ds.boost.d_9)
dds.boost.d_9 <- DESeq(ds.boost.d_9, test="Wald")
res.dds.boost.d_9 <- results(dds.boost.d_9, cooksCutoff = FALSE)
summary(res.dds.boost.d_9)
## 
## out of 686 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 71, 10%
## LFC < 0 (down)     : 31, 4.5%
## outliers [1]       : 0, 0%
## low counts [2]     : 145, 21%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
# Day 0
ds.boost.d0 <- phyloseq_to_deseq2(ps1.d0, ~randomization_arm + d7_rota_boost_updated)
geoMeans.ds.boost.d0 <- apply(counts(ds.boost.d0), 1, gm_mean)
ds.boost.d0 <- estimateSizeFactors(ds.boost.d0, geoMeans = geoMeans.ds.boost.d0)
dds.boost.d0 <- DESeq(ds.boost.d0, test="Wald")
res.dds.boost.d0 = results(dds.boost.d0, cooksCutoff = FALSE)
summary(res.dds.boost.d0)
## 
## out of 524 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 71, 14%
## LFC < 0 (down)     : 13, 2.5%
## outliers [1]       : 0, 0%
## low counts [2]     : 192, 37%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
# Day 7
ds.boost.d7 <- phyloseq_to_deseq2(ps1.d7, ~randomization_arm + d7_rota_boost_updated)
geoMeans.ds.boost.d7 <- apply(counts(ds.boost.d7), 1, gm_mean)
ds.boost.d7 <- estimateSizeFactors(ds.boost.d7, geoMeans = geoMeans.ds.boost.d7)
dds.boost.d7 <- DESeq(ds.boost.d7, test="Wald")
res.dds.boost.d7 = results(dds.boost.d7, cooksCutoff = FALSE)
summary(res.dds.boost.d7)
## 
## out of 792 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 136, 17%
## LFC < 0 (down)     : 19, 2.4%
## outliers [1]       : 0, 0%
## low counts [2]     : 414, 52%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
# Tabulate results
# Day -9
nrow(res.dds.boost.d_9)
## [1] 1588
df1.boost <- as.data.frame(res.dds.boost.d_9[ which(res.dds.boost.d_9$padj < 0.05), ])
nrow(df1.boost)
## [1] 99
df1.boost <- as.data.frame(df1.boost[ which(df1.boost$baseMean > 100), ])
nrow(df1.boost)
## [1] 9
df1.boost <- rownames_to_column(df1.boost, var = "ASV")
df1.boost$Comparison <- "Boost: Day -9"
df1.boost$Variable <- "Boost"

# Day 0
nrow(res.dds.boost.d0)
## [1] 1588
df2.boost <- as.data.frame(res.dds.boost.d0[ which(res.dds.boost.d0$padj < 0.05), ])
nrow(df2.boost)
## [1] 84
df2.boost <- as.data.frame(df2.boost[ which(df2.boost$baseMean > 100), ])
nrow(df2.boost)
## [1] 2
df2.boost <- rownames_to_column(df2.boost, var = "ASV")
df2.boost$Comparison <- "Boost: Day 0"
df2.boost$Variable <- "Boost"

# Day 7
nrow(res.dds.boost.d7)
## [1] 1588
df3.boost <- as.data.frame(res.dds.boost.d7[ which(res.dds.boost.d7$padj < 0.05), ])
nrow(df3.boost)
## [1] 150
df3.boost <- as.data.frame(df3.boost[ which(df3.boost$baseMean > 100), ])
nrow(df3.boost)
## [1] 9
df3.boost <- rownames_to_column(df3.boost, var = "ASV")
df3.boost$Comparison <- "Boost: Day 7"
df3.boost$Variable <- "Boost"

# Combine all differential abundance tables
df.all.boost <- rbind(df1.boost, df2.boost, df3.boost)
nrow(df.all.boost)
## [1] 20
dfs.boost <- list(df1.boost, df2.boost, df3.boost)
df.unique.boost <- join_all(dfs.boost, type = "full", by = "ASV")
nrow(df.unique.boost)
## [1] 17
# Bind taxonomy
df.all.boost <- left_join(df.all.boost, ps1.rdp.tax, by = "ASV")
df.unique.boost <- left_join(df.unique.boost, ps1.rdp.tax, by = "ASV")
write.table(df.unique.boost, file = "./results/df_unique_boost.txt", sep = "\t")
write.table(df.unique.boost, file = "./results/df_all_boost.txt", sep = "\t")

p.boost.point <- ggplot(subset(df.unique.boost, Comparison != "Boost: Day -9"), aes(x = log2FoldChange, y = Family, color = Phylum)) +
  geom_point(aes(size = log10(baseMean)), alpha = 0.7) +
  facet_grid(~Comparison) +
  geom_vline(xintercept = 0, lty = 2) +
  theme(legend.position = "NULL") +
  xlim(-40, 40) +
  scale_color_brewer(palette = "Dark2") +
  geom_errorbarh(aes(xmax = log2FoldChange + lfcSE, xmin = log2FoldChange - lfcSE), size=0.5, height = 0.3) +
  labs(x=expression(log[2]*" fold-change")) +
  labs(size=expression(log[10]*" base mean"))
p.boost.point

## Differential abundance for shedding at each time point

# Day -9
ds.shed.d_9 <- phyloseq_to_deseq2(ps1.d_9, ~randomization_arm + Shedding)
geoMeans.ds.shed.d_9 <- apply(counts(ds.shed.d_9), 1, gm_mean)
ds.shed.d_9 <- estimateSizeFactors(ds.shed.d_9, geoMeans = geoMeans.ds.shed.d_9)
dds.shed.d_9 <- DESeq(ds.shed.d_9, test="Wald")
res.dds.shed.d_9 <- results(dds.shed.d_9, cooksCutoff = FALSE)
summary(res.dds.shed.d_9)
## 
## out of 767 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 37, 4.8%
## LFC < 0 (down)     : 97, 13%
## outliers [1]       : 0, 0%
## low counts [2]     : 218, 28%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
# Day 0
ds.shed.d0 <- phyloseq_to_deseq2(ps1.d0, ~randomization_arm + Shedding)
geoMeans.ds.shed.d0 <- apply(counts(ds.shed.d0), 1, gm_mean)
ds.shed.d0 <- estimateSizeFactors(ds.shed.d0, geoMeans = geoMeans.ds.shed.d0)
dds.shed.d0 <- DESeq(ds.shed.d0, test="Wald")
res.dds.shed.d0 = results(dds.shed.d0, cooksCutoff = FALSE)
summary(res.dds.shed.d0)
## 
## out of 515 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 39, 7.6%
## LFC < 0 (down)     : 22, 4.3%
## outliers [1]       : 0, 0%
## low counts [2]     : 201, 39%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
# Day 7
ds.shed.d7 <- phyloseq_to_deseq2(ps1.d7, ~randomization_arm + Shedding)
geoMeans.ds.shed.d7 <- apply(counts(ds.shed.d7), 1, gm_mean)
ds.shed.d7 <- estimateSizeFactors(ds.shed.d7, geoMeans = geoMeans.ds.shed.d7)
dds.shed.d7 <- DESeq(ds.shed.d7, test="Wald")
res.dds.shed.d7 = results(dds.shed.d7, cooksCutoff = FALSE)
summary(res.dds.shed.d7)
## 
## out of 813 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 93, 11%
## LFC < 0 (down)     : 4, 0.49%
## outliers [1]       : 0, 0%
## low counts [2]     : 322, 40%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
# Tabulate results
# Day -9
nrow(res.dds.shed.d_9)
## [1] 1588
df1.shed <- as.data.frame(res.dds.shed.d_9[ which(res.dds.shed.d_9$padj < 0.05), ])
nrow(df1.shed)
## [1] 130
df1.shed <- as.data.frame(df1.shed[ which(df1.shed$baseMean > 100), ])
nrow(df1.shed)
## [1] 8
df1.shed <- rownames_to_column(df1.shed, var = "ASV")
df1.shed$Comparison <- "Shedding: Day -9"
df1.shed$Variable <- "Shedding"

# Day 0
nrow(res.dds.shed.d0)
## [1] 1588
df2.shed <- as.data.frame(res.dds.shed.d0[ which(res.dds.shed.d0$padj < 0.05), ])
nrow(df2.shed)
## [1] 61
df2.shed <- as.data.frame(df2.shed[ which(df2.shed$baseMean > 100), ])
nrow(df2.shed)
## [1] 4
df2.shed <- rownames_to_column(df2.shed, var = "ASV")
df2.shed$Comparison <- "Shedding: Day 0"
df2.shed$Variable <- "Shedding"

# Day 7
nrow(res.dds.shed.d7)
## [1] 1588
df3.shed <- as.data.frame(res.dds.shed.d7[ which(res.dds.shed.d7$padj < 0.05), ])
nrow(df3.shed)
## [1] 93
df3.shed <- as.data.frame(df3.shed[ which(df3.shed$baseMean > 100), ])
nrow(df3.shed)
## [1] 5
df3.shed <- rownames_to_column(df3.shed, var = "ASV")
df3.shed$Comparison <- "Shedding: Day 7"
df3.shed$Variable <- "Shedding"

# Combine all differential abundance tables
df.all.shed <- rbind(df1.shed, df2.shed, df3.shed)
nrow(df.all.shed)
## [1] 17
dfs.shed <- list(df1.shed, df2.shed, df3.shed)
df.unique.shed <- join_all(dfs.shed, type = "full", by = "ASV")
nrow(df.unique.shed)
## [1] 16
# Bind taxonomy
df.all.shed <- left_join(df.all.shed, ps1.rdp.tax, by = "ASV")
df.unique.shed <- left_join(df.unique.shed, ps1.rdp.tax, by = "ASV")
write.table(df.unique.shed, file = "./results/df_unique_shed.txt", sep = "\t")
write.table(df.unique.shed, file = "./results/df_all_shed.txt", sep = "\t")

p.shed.point <- ggplot(subset(df.unique.shed, Comparison != "Shedding: Day -9"), aes(x = log2FoldChange, y = Family, color = Phylum)) +
  geom_point(aes(size = log10(baseMean)), alpha = 0.7) +
  facet_grid(~Comparison) +
  geom_vline(xintercept = 0, lty = 2) +
  xlim(-40, 40) +
  theme(legend.position = "NULL") +
  scale_color_brewer(palette = "Dark2") +
  geom_errorbarh(aes(xmax = log2FoldChange + lfcSE, xmin = log2FoldChange - lfcSE), size=0.5, height = 0.3) +
  labs(x=expression(log[2]*" fold-change")) +
  labs(size=expression(log[10]*" base mean"))
p.shed.point

nrow(df.all.boost)
## [1] 20
nrow(df.all.shed)
## [1] 17
df.all.boost_shed <- rbind(df.all.boost, df.all.shed)
df.all.boost_shed.distinct <- (distinct(df.all.boost_shed, ASV, .keep_all = TRUE))
nrow(df.all.boost_shed.distinct)
## [1] 23
df.all.boost.counts <- inner_join(df.all.boost, rlog.all, by = "ASV")
nrow(df.all.boost.counts)
## [1] 2000
df.all.shed.counts <- inner_join(df.all.shed, rlog.all, by = "ASV")
nrow(df.all.shed.counts)
## [1] 1700
# Smoother plots
p.diff.ab.boost.smooth <- ggplot(df.all.boost.counts, aes(x = day, y = Abundance, group = d7_rota_boost_updated, color = d7_rota_boost_updated)) +
  geom_jitter(size = 1, alpha = 0.6, width = 0.5) +
  scale_y_log10() +
  labs(color = "Boost") +
  theme(legend.position = "NULL") +
  facet_wrap(Phylum.x~Family.x) +
  stat_smooth(data = subset(df.all.boost.counts, d7_rota_boost_updated == "No", method = "loess")) +
  stat_smooth(data = subset(df.all.boost.counts, d7_rota_boost_updated == "Yes", method = "loess")) +
  labs(y = "Abundance (rlog)", x = "Day")

p.diff.ab.shed.smooth <- ggplot(df.all.shed.counts, aes(x = day, y = Abundance, group = Shedding, color = Shedding)) +
  geom_jitter(size = 1, alpha = 0.6, width = 0.5) +
  scale_y_log10() +
  theme(legend.position = "NULL") +
  facet_wrap(Phylum.x~Family.x) +
  stat_smooth(data = subset(df.all.shed.counts, Shedding == "No shedding", method = "loess")) +
  stat_smooth(data = subset(df.all.shed.counts, Shedding == "Shedding", method = "loess")) +
  labs(y = "Abundance (rlog)", x = "Day")
ggarrange(ggarrange(p.boost.point, p.shed.point, nrow = 2, labels = c("A)", "C)")),
          ggarrange(p.diff.ab.boost.smooth, p.diff.ab.shed.smooth, nrow = 2, labels = c("B)", "D)")), widths = c(1,1.25), ncol = 2)

# Display current R session information
sessionInfo()
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.5
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] bindrcpp_0.2.2              DESeq2_1.21.1              
##  [3] SummarizedExperiment_1.11.1 DelayedArray_0.7.0         
##  [5] BiocParallel_1.15.0         matrixStats_0.53.1         
##  [7] Biobase_2.41.0              GenomicRanges_1.33.1       
##  [9] GenomeInfoDb_1.17.0         IRanges_2.15.4             
## [11] S4Vectors_0.19.2            BiocGenerics_0.27.0        
## [13] pairwiseAdonis_0.0.1        cluster_2.0.7-1            
## [15] data.table_1.11.4           ggpubr_0.1.6               
## [17] magrittr_1.5                microbiome_1.3.0           
## [19] plotly_4.7.1                knitr_1.20                 
## [21] gridExtra_2.3               vegan_2.5-2                
## [23] lattice_0.20-35             permute_0.9-4              
## [25] RColorBrewer_1.1-2          phyloseq_1.25.0            
## [27] plyr_1.8.4                  reshape2_1.4.3             
## [29] forcats_0.3.0               stringr_1.3.1              
## [31] dplyr_0.7.5                 purrr_0.2.5                
## [33] readr_1.1.1                 tidyr_0.8.1                
## [35] tibble_1.4.2                ggplot2_2.2.1              
## [37] tidyverse_1.2.1            
## 
## loaded via a namespace (and not attached):
##  [1] readxl_1.1.0           backports_1.1.2        Hmisc_4.1-1           
##  [4] igraph_1.2.1           lazyeval_0.2.1         splines_3.5.0         
##  [7] crosstalk_1.0.0        digest_0.6.15          foreach_1.4.4         
## [10] htmltools_0.3.6        checkmate_1.8.5        memoise_1.1.0         
## [13] Biostrings_2.49.0      annotate_1.59.0        modelr_0.1.2          
## [16] colorspace_1.3-2       blob_1.1.1             rvest_0.3.2           
## [19] haven_1.1.1            crayon_1.3.4           RCurl_1.95-4.10       
## [22] jsonlite_1.5           genefilter_1.63.0      bindr_0.1.1           
## [25] survival_2.42-3        iterators_1.0.9        ape_5.1               
## [28] glue_1.2.0             gtable_0.2.0           zlibbioc_1.27.0       
## [31] XVector_0.21.0         Rhdf5lib_1.3.0         scales_0.5.0          
## [34] DBI_1.0.0              Rcpp_0.12.17           viridisLite_0.3.0     
## [37] xtable_1.8-2           htmlTable_1.12         foreign_0.8-70        
## [40] bit_1.1-14             Formula_1.2-3          htmlwidgets_1.2       
## [43] httr_1.3.1             acepack_1.4.1          pkgconfig_2.0.1       
## [46] XML_3.98-1.11          nnet_7.3-12            locfit_1.5-9.1        
## [49] tidyselect_0.2.4       labeling_0.3           rlang_0.2.1           
## [52] later_0.7.3            AnnotationDbi_1.43.0   munsell_0.4.3         
## [55] cellranger_1.1.0       tools_3.5.0            cli_1.0.0             
## [58] RSQLite_2.1.1          ade4_1.7-11            broom_0.4.4           
## [61] evaluate_0.10.1        biomformat_1.7.0       yaml_2.1.19           
## [64] bit64_0.9-7            nlme_3.1-137           mime_0.5              
## [67] xml2_1.2.0             compiler_3.5.0         rstudioapi_0.7        
## [70] ggsignif_0.4.0         geneplotter_1.59.0     stringi_1.2.2         
## [73] highr_0.6              Matrix_1.2-14          psych_1.8.4           
## [76] multtest_2.37.0        pillar_1.2.3           cowplot_0.9.2         
## [79] bitops_1.0-6           httpuv_1.4.3           R6_2.2.2              
## [82] latticeExtra_0.6-28    promises_1.0.1         codetools_0.2-15      
## [85] MASS_7.3-50            assertthat_0.2.0       rhdf5_2.25.0          
## [88] rprojroot_1.3-2        mnormt_1.5-5           GenomeInfoDbData_1.1.0
## [91] mgcv_1.8-23            hms_0.4.2              grid_3.5.0            
## [94] rpart_4.1-13           rmarkdown_1.9          shiny_1.1.0           
## [97] lubridate_1.7.4        base64enc_0.1-3